Systems and methods for assisting individuals in a behavioral-change program

ABSTRACT

Methods and systems of enhancing an electronic interaction between a behavioral-modification program and a user in the program by providing customized content specific to the user. The systems and methods allow for coach-counselor assistance to the individual-user or for automated content delivery.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/138,491 filed Dec. 30, 2020, which claims benefit of priority to U.S.provisional application No. 62/955,214 filed Dec. 30, 2019; and62/955,219 filed Dec. 30, 2019, and is related to International PatentApplication No. PCT/US2020/067530, filed Dec. 30, 2020, the entirety ofeach of which are incorporated by reference.

FIELD OF THE INVENTION

The present disclosure relates to methods and systems of enhancing anelectronic interaction between a behavioral-modification program and auser in the program by providing customized content specific to theuser. The systems and methods allow for coach-counselor assistance tothe individual-user or for automated content delivery.

BACKGROUND

Behavioral modification programs include those programs that attempt toassist an individual enrolled in or following the program (e.g., anindividual-user) to lessen or cease undesirable behaviors in an attemptto improve physical and/or mental health. Many behavior modificationprograms attempt to change behavior or reduce undesired behaviors bymeans of techniques that include negative and positive reinforcement,imposing limitations, goal setting, and conditioning of theindividual-user.

Because the success rate of behavioral modification programs isultimately dependent on the actions of the individual-user in theprogram, providing support for those individual-users as theyparticipate in the program is extremely important. Therefore, effectivecoaching can often improve success rates of such behavioral-modificationprograms.

In many cases, unhealthy behaviors are learned over significant timeperiods. Therefore, an individual who is attempting to unlearn suchbehaviors or who is attempting to alter future behaviors can facedifferent levels of difficulty that vary depending on many factorsspecific to that individual-user. Therefore, any coach who attempts toassist that individual-user will be more effective if the coach canemploy coaching techniques that incorporate information specific to thatindividual-user, whether that information is the individual's backgroundor the individual-user's activity/behavior in the program. For example,an individual-user who is in an early stage of a behavioral-modificationprogram can require a different type of coaching support than anindividual-user who has completed a behavioral-modification program butremains engaged for continued compliance with the modified behavior.Moreover, coaching support for an individual-user that is strictlyfollowing the program will vary from the coaching support needed byanother user that fails to remain in compliance with the program.

There remains a need to provide improved and effective coaching supportfor individual users of any behavioral modification programs. While thepresent disclosure discusses a smoking cessation program, the presentdisclosure can benefit any number of behavior modification programs,including but not limited to those programs that assist individuals withvaping cessation, nicotine addiction, weight loss, medicationcompliance, addiction, handling depression, increasing physical and/ormental activity, etc.

SUMMARY OF THE INVENTION

The system and methods described herein allow for assisting anindividual in a behavioral-modification program through personalizedcoaching and personalized program feedback both of which are unique toeither the individual user or the activity of the individual user in theprogram. In one example, the behavioral-modification program is asmoking cessation program. However, additional variations of the methodsand systems described herein can be applied to any number of behavioralmodification programs. Yet additional variations of the methods andsystems disclosed herein include behavioral-modification programs thatuse biological feedback/measurements from the individual user.

The present disclosure includes methods of enhancing an electronicinteraction between a coach-counselor assisting an individual-userparticipating in a behavioral-modification program. For example, such amethod can include providing electronic access to a database ofinformation during the electronic interaction between thecoach-counselor and the individual-user, where the database ofinformation includes a plurality of user-specific input data specific tothe individual-user, where at least a portion of the plurality ofuser-specific input data is previously collected; electronicallydisplaying a background data to the coach-counselor during theelectronic interaction, where the background data includes a historicalinformation regarding an activity of the individual-user in thebehavioral-modification program, to permit the coach-counselor a reviewthe historical information regarding the individual-user during theelectronic interaction; electronically supplying the coach-counselorwith at least one prompt of a communication topic from a database ofgeneric information applicable to the behavioral-modification program,where the at least one prompt improves efficiency and accuracy of aninteraction between the coach-counselor and the individual-user toprovides a coaching topic for the coach-counselor to assist theindividual-user in the behavioral-modification program; andelectronically transmitting the at least one prompt to theindividual-user as a coach-message.

Variations of the methods can include user-specific data that includesat least one of a subset of individual-user psychographic information, asubset of individual-user personal information, a subset ofindividual-user biological input data, and/or a combination thereof. Inadditional variations, the user-specific data includes a subset ofindividual-user biological input data with at least one of a subset ofindividual-user psychographic information and a subset ofindividual-user personal information, any additional information and/ora combination thereof.

Variations of the methods and systems described herein can includemethods where electronically transmitting the at least one prompt occursautomatically without input from the coach-counselor. Alternatively, orin combination, electronically transmitting the at least one promptrequires an input from the coach-counselor.

Variations of the methods and systems can further require establishingan electronic reporting interface for the coach-counselor, where theelectronic reporting interface allows the coach-counselor toelectronically access a database of batch data that includes informationfrom a plurality of users who participated in thebehavioral-modification program.

The database of information can further include a behavior summary ofthe individual-user, where the behavior summary comprises an associationof the biological input data from the individual-user with at least oneof a plurality of behavioral data supplied by the individual-user wherethe behavioral data is non-biologic.

The database of information can be updated automatically by monitoringthe user's activities and/or a coach-counselor can update the databaseof information about the individual-user.

In an additional variation, the subset of individual-user personalinformation in the database of information includes information from thegroup consisting of background, traits, demographics, and previous notesabout the individual-user. Variations of the method can include a subsetof individual-user psychographic information that includes milestonesand targets of the user.

In variations of the method and systems displaying the data includesdisplaying a conversation history between the individual-user and thecoach-counselor.

The prompts can be reusable prompts that are applicable to multiplealternate users. Additionally, or alternatively, the at least one promptcan comprise a partially written statement, wherein the coach-counselormust complete the partially written statement prior to sending thestatement to the individual-user.

Variations of the methods include the coach-counselor to select at leastone prompt, and wherein the at least one prompt comprises encodedvariables that are pre-filled when the at least one prompt is selectedby the coach-counselor.

The methods and systems described herein can include checks, such aswhere the prompt includes placeholders and the method further comprisingpreventing electronically transmitting the at least one prompt untilcoach-counselor replaces the placeholders with text.

Another variation of the method includes tagging the coach-message toassign a relevant category. The relevant category can include a triggeror a behavior.

In some variations, coach-message is added to the database ofinformation about the user and can be made either private or public.

Additional variations of the method include enabling the coach-counselorto select data from the database of information comprises enabling thecoach-counselor to search by content of the at least one prompt.

The prompts discussed herein can be altered to maintain stylisticsimilarity to the coach-counselor.

In additional variations, the method can further include selecting anautomated message based on an inquiry from the individual-user andautomatically transmitting the automated message to the individual-user.

The behavior modification programs disclosed herein can includepreventing a behavior selected from the group consisting of smokingcigarettes, vaping, consuming alcohol, use of tobacco, use of narcotics.

The present disclosure also includes methods of providing customizedcontent to an individual-user participating in a behavioral-modificationprogram. An example of such a method includes providing a database ofinformation comprised of a plurality of user-specific data specific tothe individual-user, where at least a portion of the plurality ofuser-specific input data is previously collected; electronicallymonitoring an activity of the individual-user; using the activity tocustomize program-related content comprising an electronic media contentfrom a database of generic information applicable to thebehavioral-modification program; electronically transmitting theprogram-related content to the individual-user as an electronic message;and monitoring the individual-user's electronic interaction with theprogram-related content.

Again, variations of the methods can include user-specific data thatincludes at least one of a subset of individual-user psychographicinformation, a subset of individual-user personal information, a subsetof individual-user biological input data, and/or a combination thereof.In additional variations, the user-specific data includes a subset ofindividual-user biological input data with at least one of a subset ofindividual-user psychographic information and a subset ofindividual-user personal information, any additional information and/ora combination thereof.

Variations of the method include an electronic message that furtherincludes at least one data item from one of the subsets ofindividual-user psychographic information, the subset of individual-userpersonal information, or a subset of individual-user biological inputdata.

Electronically transmitting the program-related content to theindividual user can occur automatically or can require an input from theindividual user. In additional variations, the database of informationfurther includes a behavior summary of the individual, where thebehavior summary comprises an association of the biological input datafrom the individual with at least one of a plurality of behavioral datasupplied by the individual where the behavioral data is non-biologic.

The subset of individual-user personal information in the database ofinformation can include information from the group consisting ofbackground, traits, demographics, and previous notes about theindividual-user. The subset of individual-user psychographic informationin the database of information can include milestones and targets.

The methods can further include adding the program-related content tothe database of information about the user. In additional variations,enabling the coach-counselor to select data from the database ofinformation comprises enabling the coach-counselor to search by contentof the at least one prompt.

This application is related to the following commonly assigned patentsand applications. Such patents include U.S. Pat. No. 10,306,922 issuedon Jun. 4, 201; U.S. Pat. No. 9,861,126 issued on Jan. 9, 2018; U.S.Pat. No. 10,674,761 issued on Jun. 9, 2020; U.S. Pat. No. 10,206,572issued on Feb. 19, 2019; U.S. Pat. No. 10,335,032 issued on Jul. 2,2019; U.S. Pat. No. 10,674,913 issued on Jun. 9, 2020, and U.S. Pat. No.10,306,922 issued on Jun. 4, 2019. Such applications include: Ser. No.16/889,617 published as US20200288785 on Sep. 17, 2020; Ser. No.15/782,718 published as US20190113501 on Apr. 18, 2019; and Ser. No.16/890,253 published as US20200288979 on Sep. 17, 2020. The entirety ofeach of the above patents and applications is incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an individual-user employing an electronic personaldevice that is configured to provide biological data/measurements fromthe individual.

FIG. 2A illustrates the methods and systems for enhanced coaching of anindividual require building and/or compiling one or more databases thatinclude information specific to the user.

FIGS. 2B and 2C illustrate non-exhaustive lists of inputs that drivedata subsets 72, 74 building one or more databases of informationspecific to the individual-user.

FIG. 3A represents compiling one or more databases using additionalsubsets of biologic data as well as application, apps, and/or sensordata.

FIGS. 3B and 3C represent various inputs used to produce biologic andapp/sensor data subsets.

FIG. 4 illustrates a conceptual electronic interaction between acounselor-coach attending to an individual user that is participating abehavior-modification program.

FIG. 5A illustrates one example of a display, such as via an electronicdisplay, of information provided to a coach-counselor to enhance aninteraction when assisting an individual-user during abehavioral-modification program.

FIGS. 5B and 5C illustrate one variation of a display in accordance withthe display discussed in FIG. 5A where a coach-counselor has access toinformation intended to improve an interaction with a user.

FIG. 6 is a conceptual illustration of an embodiment of a system/methodfor enhancing a direct electronic interaction between one or moresystems/servers of a behavioral-modification program and anindividual-user.

FIGS. 7A and 7B also illustrate informational data cards, which areselected to provide personalized information specific to the user.

FIG. 8 illustrates an electronic interface having differentinformational cards 44 that display information selected and customizedfor the user based on any subset of data as discussed above.

FIG. 9 depicts an illustrative system including a wearable device, amobile device, and a remote server in communication with the wearabledevice and the mobile device in accordance with some embodiments of thedisclosure.

FIG. 10 depicts another illustrative system including a wearable deviceand a remote server in communication with the wearable device inaccordance with some embodiments of the disclosure.

FIG. 11 depicts illustrative light absorption curves for various typesof hemoglobin, allowing for measurement of the levels ofcarboxyhemoglobin (SpCO) and oxyhemoglobin (SpO2) using aphotoplethysmography (PPG) sensor in accordance with some embodiments ofthe disclosure.

FIG. 12 depicts a chart for a patient's varying levels of SpCO for atypical five-day monitoring period prior to commencing a smokingcessation program in accordance with some embodiments of the disclosure.

FIG. 13 depicts a trend of SpCO levels and smoking triggers for apatient over a typical day prior to commencing a smoking cessationprogram in accordance with some embodiments of the disclosure.

FIG. 14 depicts a data structure for storing SpCO levels and smokingtriggers for a patient over a typical day in accordance with someembodiments of the disclosure.

FIG. 15 depicts an illustrative flow diagram for detecting smokingbehavior of a patient in accordance with some embodiments of thedisclosure.

FIG. 16 depicts a sample report after a five-day evaluation for apatient in accordance with some embodiments of the disclosure.

FIG. 17 depicts an illustrative chart of patient SpCO levels duringrun-in and quit program in accordance with some embodiments of thedisclosure.

FIG. 18 depicts an illustrative smart phone app screen showingmeasurements such as SpCO, SpO2, heart rate, respiratory rate, bloodpressure, and body temperature in accordance with some embodiments ofthe disclosure.

FIG. 19 depicts an illustrative smart phone app screen for receivingpatient entered data in accordance with some embodiments of thedisclosure.

FIG. 20 depicts an illustrative smart phone app screen implementing asmoking prevention protocol in accordance with some embodiments of thedisclosure.

FIG. 21 depicts an illustrative smart phone app screen for presentingthe quit process as a game for the patient in accordance with someembodiments of the disclosure.

FIG. 22 depicts an illustrative flow diagram for predicting andpreventing an expected smoking event in accordance with some embodimentsof the disclosure.

FIG. 23 depicts an illustrative flow diagram for step 1414 in FIG. 22for determining whether a prevention protocol was successful inaccordance with some embodiments of the disclosure.

FIG. 24 depicts an illustrative flow diagram for a one-time measurementof the patient's SpCO level using a PPG sensor in accordance with someembodiments of the disclosure.

FIG. 25 depicts an illustrative flow diagram for detecting a smokingevent in accordance with some embodiments of the disclosure.

FIG. 26 depicts an illustrative flow diagram for applying one or moreperturbations to a model for a patient's smoking behavior in accordancewith some embodiments of the disclosure.

FIG. 27 illustrates another variation of a system and/or method foraffecting an individual's smoking behavior using a number of the aspectsdescribed herein as well as further quantifying an exposure of theindividual to cigarette smoke.

FIG. 28A illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 27.

FIG. 28B illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 27.

FIG. 29 illustrates an example of a dataset used to determine the eCOcurve over a period of time where the eCO attributable to the smokingbehavior of the individual can be quantified over various intervals oftime to determine an eCO Burden or eCO Load for each interval.

FIG. 30 illustrates an example of displaying the biometric data as wellas various other information for assessing the smoking behavior of theindividual.

FIG. 31 shows another variation of a dashboard displaying similarinformation to that shown in FIG. 30.

FIGS. 32A to 32C illustrate another variation of a dataset comprisingexhaled carbon monoxide, collection time, and cigarette data quantifiedand displayed to benefit the individual attempting to understand theirsmoking behavior.

FIGS. 33A-33H illustrate another variation of the systems and methodsdescribed above used to implement a treatment plan for identifying asmoking behavior of an individual for ultimately assisting theindividual with smoking cessation and maintaining the individual'sstatus as a non-smoker.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure includes methods for enhanced coaching of anindividual-user that is participating in a behavioral-modificationprogram. The coaching can occur using an actual coach, who is anindividual that is trained to assist the user during the program.Alternatively, or in combination, coaching can include automatedelectronic communication either pushed or requested by the individual,where the automated electronic communication can provide repeatedinteraction with the individual-user to maintain engagement with theprogram. As noted herein, the coaching includes customized informationas well as generic information. For example, customized information isinformation that is intended to specifically apply to an individual userbased on any number of criteria unique to that user. While genericinformation can comprise information that applies to one or more usersregardless of their specific situation.

In a first variation, the methods and systems for enhanced coaching ofan individual are discussed specific to a smoking cessation program.However, the methods and systems can apply to anybehavioral-modification program intended to increase a health of theindividual and/or a well-being of the user.

The behavioral-modification programs described herein rely on electroniccommunication to facilitate exchange of information between anindividual-user 10 and the behavioral modification program (e.g., theprogram's computer/database systems and/or a live coach.) For example,FIG. 1 represents an illustration of an individual-user 10 employing anelectronic personal device that is configured to provide biologicaldata/measurements from the individual. While the method and systemsinclude any personal electronic device capable of providing biologicaldata/measurements, for purposes of illustration, FIG. 1 shows a smartwatch 52 or breath sensor 54 that can be used to communicate biologicaldata to a cloud server 60 either directly or through any intermediarydevice such as a smart-phone 56 or other personal computing device. Inthe variation discussed below, which provides a smoking cessationprogram as the behavioral-modification program, the user 10 employs aportable device 56 that obtains a plurality of samples of exhaled airfrom the individual with sensors that measure an amount of carbonmonoxide within the sample of exhaled air (also referred to as exhaledcarbon monoxide or ECO). The biological input data can comprise datameasured by a device (e.g., exhaled breath via device 54).Alternatively, or in combination, the biological data can comprise datathat is entered manually by the user 10 as will be discussed below.

In a first variation, as conceptually illustrated in FIG. 2A, themethods and systems for enhanced coaching of an individual requiresbuilding and/or compiling one or more databases 70 that includesinformation specific to the user 10. This data can include, but is notlimited, to data subsets (e.g., 72, 74, 76) that builds one or moredatabases 70. In some variations, submission of biologic data 76 willnot occur until a user is engaged with the program.

The transmission 62 or entering of the data 72, 74, 76, into the one ormore databases 70 can occur via any number of methods. For example, thedata 72, 74, 76 can be compiled prior to or during the initial stages ofa behavioral-modification program by one or more individuals associatedwith the program. Alternatively, or in combination, the user 10 can someor all of the data 72, 74, 76 using an electronic interface. Theuser-specific database 70 (which may include one or more databases) canbe compiled and/or updated over any timeframe. However, thebehavioral-modification program can establish a minimal level ofinformation required to start or enroll the individual in the program.While FIG. 2 illustrates data transmission 62 to a cloud or cloud server60, variations of the method and systems within this disclosure caninclude local storage of the database.

FIGS. 2B and 2C illustrate non-exhaustive lists of inputs 73, 75 thatdrive the data subsets 72, 74 for building one or more databases ofinformation specific to the individual-user. As shown, in FIG. 2B userpersonal information 72 can comprise information, in this example,demographic information. Typically, such user personal information 72comprises information specific to a history or identity of theindividual user. Such informational inputs include, but are not limitedto: gender, age, tobacco products used and quantity used, previousattempts at quitting, geographic area, languages spoken, culture oraspects of culture, ethnicity, country, nicotine replacement therapyexperience and history, income, education, socioeconomic status, familyhistory of tobacco usage, weight or body mass indicator, family memberswho smoke, marital status, children, living situation at home, communityfactors (e.g., poverty, crime, education quality), access to healthcare, and health insurance.

FIG. 2C shows an example of a number of inputs 75 that can be used tocollect a specific subset of data comprising psychographic informationof the individual user. The psychographic inputs 75 allow for building adatabase of psychographic information 74 allowing for the study andclassification of the individual-user according to his/her attitudes,aspirations, and other psychological criteria, in relation to thebehavioral-modification program. Again, why the psychographic inputswill vary depending upon the specific behavior-modification program, ina smoking cessation program, such psychographic inputs 75 can include,but are not limited to, goal toward changing tobacco habits; motivationto quit; well-being; confidence in quitting;nicotine-replacement-therapy preferences; personal crisis; situationalfactor (e.g., traveling for holidays, stressful work season); andcomorbid mental or physical health disorders (e.g., major depression,obesity).

FIG. 3A represent a condition where the one or more databases arecompiled using additional subsets of biologic data 76 as well asapplication, apps, and/or sensor data 78. As noted above, the use ofadditional biologic data 76 and app/sensor data 78 often occurs duringparticipating of the user 10 in the behavior-modification program andafter the database is compiled as illustrated in FIG. 2A. However, themethods and systems described herein can include any sequence ofcompiling the database(s).

FIGS. 3B and 3C represent various inputs 77, 79 used to produce biologic76 and app/sensor 78 data subsets. The biologic inputs 77 can be anyinput related to the user. Typically, the biologic data is entered usinga personal device (e.g., 52 and/or 54 as shown in FIG. 1). However, thebiologic data 76 can be produced or measured in any manner required bythe particular data that are useful to the behavior-modificationprogram. Examples of biologic inputs 77 for use in a smoking-cessationbehavioral modification program, include but are not limited to: carbonmonoxide (CO) levels; breath volume; oxygen levels; blood pressure; andhemoglobin A1c measurements. It is noted that the application data 78can include data that is actively submitted by the user or data that ispassively recorded by the system (e.g., duration in the program, timebetween participation in the program, etc.)

FIG. 4 illustrates a conceptual electronic interaction 30 between acounselor-coach 20 attending to an individual user 10 that isparticipating a behavior-modification program. As illustrated, theelectronic interaction 30 can occur via one or more electronic devices56. While the present methods and systems also contemplates in-person orreal-time voice or messaging communication, electronic interactions 30allow for an on-demand system of coaching. The coach-counselor 20 willaccess via an electronic device 94 to a server/system 60 that improvesand enhances the interaction with the user 10. The server/system 60 isable to draw from one or more databases 70 that contain user-specificdata as discussed above. This configuration permits the coach-counselor20 to access a wide variety of data that will be helpful to thecoach-counselor 20 to provide meaningful support to the individual-user10. For example, the server/system 60 can display a plurality ofuser-specific data specific to the individual-user received from adatabase 70, where the user-specific data includes any of a subset ofindividual-user psychographic information, a subset of individual-userpersonal information, or a subset of individual-user biological inputdata. The system 60 can also provide the coach-counselor 20 with anybackground data comprising a historical information regarding theindividual-user's activity in the behavioral-modification program, topermit the coach-counselor a review the historical information regardingthe individual-user during the electronic interaction. Typically, suchbackground data includes the app/sensor usage inputs 79 (shown in FIG.3C). However, background data can also include information from aprevious session between the coach-counselor 20 and user 10. The abilityto provide a wide variety of data specific to the user allows for anynumber of coach-counselors 20 to have familiarity with the user 10.

Another feature of the system 60 is the ability to draw information fromone or more databases 90 that contain information that is specific tothe behavioral-modification program. For example, the system 60 canelectronically supply the coach-counselor 20 with at least one prompt ofa communication topic from the database 90, where the prompt orcommunication topic is generic information applicable to thebehavioral-modification program. where the at least one prompt improvesefficiency and accuracy of an interaction between the coach-counselorand the individual-user. The coach-counselor 20 will have the ability toelectronically transmit 30 a message comprising one or more prompts tothe individual-user 10. In additional variations, the coach-counselor 20will have the option of customizing the prompts prior to discussing orsending to the user 10.

FIG. 5A illustrates one example of a display, such as via an electronicdisplay 94, of information provided to a coach-counselor to enhance aninteraction when assisting an individual-user during abehavioral-modification program. FIG. 5 is intended to show a variationof the information that can be relayed to a coach-counselor. However,any variation of data subsets can be provided as needed depending on thebehavioral-modification program.

FIG. 5A illustrates a display 94 providing a plurality of biologic datain association with behavioral input of the individual, typicallycomprising application, apps, and/or sensor data 78 as previouslydiscussed. The system submits one or more prompts 40 based on acomparison of the biological data and the behavioral data 78. FIG. 5shows biological data 76 comprising Bio_Data.sub.n(1 to y), which meansthat the displayed biologic data 76 can comprise any information that isin the database discussed above. Likewise, the display can show the sameor a variety of different biological data 76. The methods and systemsdescribed herein compare various biological data 76 to behavioral data78 to generate prompts 40 for use by the coach-counselor in assistingthe specific user. As noted above, the behavioral data 78 can includebackground or other data specific to the individual such as a historicalinformation regarding the individual-user's activity in thebehavioral-modification program. The prompts 40 can be drawn frominformation in one or more databases (90 in FIG. 4) that is specific tothe behavioral-modification program. Such information can be genericinformation applicable to the behavioral-modification program. The goalof the prompts is to improve efficiency and accuracy of an interactionbetween the coach-counselor and the individual-user and to provides acoaching topic for the coach-counselor to assist the individual-user inthe behavioral-modification program.

FIG. 5A also shows the display providing additional information such asinformation 90 that is generic to the individual but related to theprogram as well as personal information 72 regarding the user.Additional variations of the systems and methods described herein caninclude display of any relevant information to the coach-counselorand/or user. Such information includes but is not limited to informationrelated to the user, related to the program, or unrelated to either theuser and/or the program.

FIGS. 5B and 5C illustrate one variation of a display 94 in accordancewith the display discussed in FIG. 5A where a coach-counselor has accessto information intended to improve an interaction with a user. As notedherein, the display 94 can include information that is specific to theindividual user. In the illustrated examples, the display includes acombination 82 of biologic data with application, apps, and/or sensordata along with prompts 40 associated with the specific combination ofinformation 82. In the first example on the left, the combination 82information informs the coach that the user already a goal to reducesmoking but has yet to pair a CO breath sensor for submission ofinformation, the information also indicates that the user received thebreath sensor. Again, such information can be a combination ofpreviously submitted data from a database specific to the user (e.g.,database 70 discussed above). This combination data comprises abackground data of historical information regarding theindividual-user's activity in the behavioral-modification program. Asshown, the system also provides various prompts 40 to thecoach-counselor that are related to the associated subset of combinationdata 82. In the example, prompts to the coach-counselor include:“Strongly recommend [to the user] pairing the CO sensor to mobile app tobetter track reduction”; “Suggest lesson [to the user]—‘Using YourBreath Sensor’” [media content provided in the smoking-cessationprogram]; and “Suggest [to the user] talking to a coach about thebenefits of the sensor and reduction.” As noted above, the prompts 40are a communication topic from a database of generic informationapplicable to the smoking cessation program. In some cases, the promptis a combination of the generic information with specific patientinformation. Regardless, the prompts are custom created discussiontopics based on the activities of the specific user. Such custom createddiscussion prompts improve efficiency and accuracy of an interactionbetween the coach-counselor and the individual-user to provides acoaching topic for the coach-counselor to assist the individual-user inthe behavioral-modification program.

FIG. 6 is another conceptual illustration of an embodiment of asystem/method for enhancing a direct electronic interaction 30 betweenone or more systems/servers 60 of a behavioral-modification program andan individual-user 10. In this variation, the system/server 60 canprovide feedback or personalized recommendations to the user 10 with orwithout a coach-counselor. The direct interaction between the user 10and the behavioral-modification program can provide real-timepersonalized advice to a user 10 while providing the user with a senseof progress, completion, and encouragement. The direct-interactionsystem described by FIG. 6 can incorporate additional information inplace of or in addition to coaching from a coach-counselor. Thisadditional information can be customized to the individual based on theindividual's interaction with the program (e.g., see app data subset 78discussed above in FIGS. 2A to 3C.) Additionally, the information cancomprise generic information regarding the behavior-modificationprogram. For purposes of clarity, the information provided to theindividual through the direct electronic interaction 30 can be referredto as program-related content. As discussed herein, the program-relatedcontent can be personalized to an individual user's goals; featureavailability of the program; the individual's prior history in and/orbefore participation in the program; and can be customized to includethe user's personal information (e.g., the user's name, the coach'sname, selected goals, etc.) Because the program-related content isprovided via an electronic communication, the system can track theuser's activity using the program-related content to determine whetherthe user's activity was started, is in progress as well as the extent ofthe progress, or completed without requiring the user to affirmativelyreport progress.

Program-related content can include any change to informational-content,including but not limited to, URL links, informational cards (e.g.,electronic “cards” discussed below, lessons, videos, challenges,activities, media content, tasks. The program-related content caninclude content that is meant to be reviewed (passive content) orinformation that requires an activity or action by the user (action).For example, some program-related content can require a call-to-action,which includes a graphical element that prompts the user to perform sometargeted action such as engaging in a challenge related to behaviormodification, an activity related to the program (e.g., contacting afamily member), or a request to obtain biological data (e.g., use thebreath sensor).

In another variation of the system and method includescustomizing/personalizing a card with data specific to the user. In sucha case, the card is generic, but has electronic placeholders that arefilled with user-specific data when sent to the user. For example, acard can generically include users of “Your top 3 reasons for thecigarettes you logged over the past 48 hours” or “Your highest/lowest COreadings over the last 48 hours.” Each user then receives a customizedcard comprising the generic information with user-specific informationincorporated into the generic message on the card.

As noted above, and as conveyed in FIG. 6, the system 60 can monitor theuser's 10 activity through the electronic interaction 30. The system canthen use algorithms to draw from information in one or more databases 90that is generic to the program. The system then selects informationalcards having content that applies to the user 10. As such, theinformational cards can be directly conveyed to the user's electronicdevice 56, or the informational cards can be compiled/added 66 to one ormore the user's personal database 70. In some variations, theinformational cards will remain on the generic database 90 but thesystem uses user-specific information stored on their personal database70 to pull the relevant generic information from the database 90.

FIGS. 7A and 7B illustrate an interface of an electronic device 56 todemonstrate an example of a user's direct interaction with thesystem/method of a smoking cessation behavioral-modification program. Asshown, the interface 56 can include any number of data subsets discussedabove, including but not limited to previously the subsets of datadisclosed herein. For example, FIGS. 7A and 7B show submitted biologicdata 76, application 78, as well as a prompt 40 discussed above. In theillustrated variation, biologic data 76 shows a measured exhaled COreading (shown as “3”) submitted from the user using a breath sensor.The application data shows an application entry of the number ofcigarettes smoked over a period of time (shown as “8”). The display caninclude any relevant text as needed as well as a control panel 46 tointeract with the system and/or coach.

FIGS. 7A and 7B also illustrate informational data cards 44, which areselected to provide personalized information specific to the user. Inthe illustrated example, FIG. 7A illustrates an information data card 44specific to someone just entering the smoking cessation behavioralmodification program (called “PIVOT”). As shown in FIG. 7B, once theuser engages the informational card 40, which can simply provideinformation or require interaction from the user, the system marks theinformational card 44 as completed. However, variations of the systemallow for the individual to revisit any informational card 44 at a laterpoint. As noted herein, informational data cards are one example ofprogram-related content (as discussed above) that is electronicallytransmitted to the user 10

FIG. 8 illustrates an electronic interface having differentinformational cards 44 that display information selected and customizedfor the user based on any subset of data as discussed above. FIG. 8 alsoillustrates that the informational cards 44 can include content 48 thatis relevant to the behavioral modification program. As noted above, thecontent can be generic to the program but selected based on specificcriteria regarding the user. Alternatively, or in combination, thecontent 48 can be customized for the user. In the example shown, thedata card 48 provides media content to explain the benefit of orderingnicotine replacement medication along with facts regarding themedication. The content 48 further allows the user to interact with oneor more buttons 49, which in the illustrated example allows the user toorder the medication.

The systems and methods described herein also allow for supporting usersin a very user-centric manner. For example, the systems and methods canallow for preserving the user's autonomy by monitoring and identifyingany card that a user ignores or does not act on. To preserve the user'sautonomy, the system and methods can delay for a specified period oftime prompting the user with the same or similar card. Such a featureallows for an “automatic snooze” of information that the user viewed butdid not act upon. The period for the delay of time can be selected bythe user and/or selected by the system configuration.

In another variation, the systems and method can include a card“expiration” function where the system stops prompting the user with acard (and/or similar cards) if the user viewed it but did not act on itfor a number of times. In one example, the system stops prompting theuser with a card after 3 impressions (where the system sees the user asreceiving the card but failing to act on it 3 times).

The systems and method can further include a card chaining functionwhere the system prompts card “A” any specified number of times and thenexpires card “A” in favor of card “B”, where card “A” and “B” can haverelated information or mutually exclusive information. The system canprompt card “B” any specified number of times and then expire card “B”in favor of card “A” or another card “C”. This sequence can be repeatedsuch that the chaining of cards can be as long or as short as desired.Such an approach maintains the coaching/counseling novel as opposed tosimply repeating the same information.

Variations of the system and methods described herein can select contentbased on user-specific information. In such a case, thecounselor-coaching prompts and/or the cards can be tailored to thatinformation. For example, some users may desire to quit on a specificday while other users desire a normal quit plan (i.e., quitting over alonger duration of time). In such a case, the system/method can assignusers to either a “fast quit” plan or a “normal quit” plan. As anexample, those users that are identified as “fast quit” users willreceive coach-counseled prompts and/or cards of a highly prioritizedlist of essential steps in the behavior-modification program. Incontrast, those users identified as “normal quit” users will getcustomized content that allows for a longer time in the plan sequence.

Additional examples of customized feedback include prompting the user inlayers. For example, the information prompted in the current week takespriority, if the user completes the interaction with cards/coaching forthat week, then the previous week's information that was incomplete canbe prompted by the coach/system. Cards/coaching can also prompt the userto keep goals and usage current. For example, the present system promptsthe user (via coaching and/or cards) for a weekly update ofcigarettes-per-day to ensure the user's goal is current at least every 2weeks. In those cases where a user is very engaged/successful in theprogram, the system can provide customized program-related content(e.g., coaching and or other information) that recommend settingaggressive behavioral goals. For example, if a user reduces smoking by50%, the system can then prompt the user to go ahead and quit. Inanother variation, a user that completes all lessons and is still activemight be prompted by the system if they want to make a change, such asreducing how much they smoke or quitting entirely.

In some embodiments, the systems and methods described herein providefor a system comprising one or more mobile devices and a server incommunication with the mobile devices. FIG. 9 shows an exemplaryembodiment 100 for such a system including device 102, device 104, andserver 106 in communication with devices 102 and 104. Device 102 assistsin detecting a patient's smoking behavior. Device 102 includes aprocessor, a memory, and a communications link for sending and receivingdata from device 104 and/or server 106. Device 102 includes one or moresensors to measure the patient's smoking behavior based on measuring oneor more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratoryrate, blood pressure, body temperature, sweating, heart ratevariability, electrical rhythm, pulse velocity, galvanic skin response,pupil size, geographic location, environment, ambient temperature,stressors, life events, and other suitable parameters. For example,device 102 may include PPG-based sensors for measuring CO, eCO, SpCO andSpO2, electrocardiography-based sensors for measuring heart rate andblood pressure, acoustic signal processing-based sensors for measuringrespiratory rate, wearable temperature sensors for measuring bodytemperature, electrodermal activity-based sensors for measuring skinconductance, electroencephalogram, implantable sensors placed in theskin, fat or muscle that measure CO and other variables, intra-oral COsensors, ambient CO sensors, and other suitable sensors. These sensorsmay have a variety of locations on or in the body for optimalmonitoring.

Device 102 may be carryable or wearable. For example, device 102 may bewearable in a manner similar to a wristwatch. In another example, device102 may be carryable or wearable and attached to the fingertip, earlobe, ear pinna, toe, chest, ankle, arm, a fold of skin, or anothersuitable body part. Device 102 may attach to the suitable body part viaclips, bands, straps, adhesively applied sensor pads, or anothersuitable medium. For example, device 102 may be attached to a fingertipvia a finger clip. In another example, device 102 may be attached to theear lobe or ear pinna via an ear clip. In yet another example, device102 may be attached to the toe via a toe clip. In yet another example,device 102 may be attached to the chest via a chest strap. In yetanother example, device 102 may be attached to the ankle via an ankleband. In yet another example, device 102 may be attached to the arm viabicep or tricep straps. In yet another example, device 102 may beattached to a fold of skin via sensor pads.

Device 102 may prompt the patient for a sample or the device, if worn,may take a sample without needing patient volition. The sampling may besporadic, continuous, near continuous, periodic, or based on any othersuitable interval. In some embodiments, the sampling is continuouslyperformed as often as the sensor is capable of making the measurement.In some embodiments, the sampling is performed continuously after a settime interval, such as five or fifteen minutes or another suitable timeinterval.

In some embodiments, device 102 includes one or more sensors to monitorSpCO using a transcutaneous method such as PPG. The transcutaneousmonitoring may employ transmissive or reflectance methods. Device 104may be a smart phone or another suitable mobile device. Device 104includes a processor, a memory, and a communications link for sendingand receiving data from device 102 and/or server 106. Device 104 mayreceive data from device 102. Device 104 may include an accelerometer, aglobal positioning system-based sensor, a gyroscopic sensor, and othersuitable sensors for tracking the described parameters. Device 104 maymeasure certain parameters, including but not limited to, movement,location, time of day, patient entered data, and other suitableparameters

The patient entered data received by device 104 may include stressors,life events, geographic location, daily events, administrations ofnicotine patches or other formulas, administrations of other drugs forsmoking cessation, and other suitable patient entered data. For example,some of the patient entered data may include information regarding phonecalls, athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. Patient use of their smart phone fortexting, calling, surfing, playing games, and other suitable use mayalso be correlated with smoking behavior, and these correlationsleveraged for predicting behavior and changing behavior. Device 104 orserver 106 (subsequent to receiving the data) may compile the data,analyze the data for trends, and correlate the data either real time orafter a specified period of time is complete. Server 106 includes aprocessor, a memory, and a communications link for sending and receivingdata from device 102 and/or device 104. Server 106 may be located remoteto devices 102 and 104 at, e.g., a healthcare provider site, or anothersuitable location.

FIG. 10 shows an exemplary embodiment 200 for a system including device202 and server 204 in communication with device 202. Device 202 assistsin detecting a patient's smoking behavior. Device 202 includes aprocessor, a memory, and a communications link for sending and receivingdata from server 204. Device 202 may be carryable or wearable. Forexample, device 202 may be wearable in a manner similar to a wristwatch.Device 202 includes one or more sensors 206 to measure the patient'ssmoking behavior based on measuring one or more of the patient's CO,eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters.

Device 202 may include one or more sensors 208 to measure certainparameters, including but not limited to, movement, location, time ofday, patient entered data, and other suitable parameters. The patiententered data may include stressors, life events, location, daily events,administrations of nicotine patches or other formulas, administrationsof other drugs for smoking cessation, and other suitable patient entereddata. The patient entered data may be received in response to a promptto the patient on, e.g., a mobile device such as device 104, or enteredwithout prompting on the patient's volition. For example, some of thepatient entered data may include information regarding phone calls,athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. Device 202 or server 204 (subsequent toreceiving the data) may compile the data, analyze the data for trends,and correlate the data either real time or after a specified period oftime is complete. Server 204 includes a processor, a memory, and acommunications link for sending and receiving data from device 202.Server 204 may be located remote to device 202 at, e.g., a healthcareprovider site, or another suitable location.

In some embodiments, device 102 or 202 includes a detector unit and acommunications unit. Device 102 or 202 may include a user interface asappropriate for its specific functions. The user interface may receiveinput via a touch screen, keyboard, or another suitable input mechanism.The detector unit includes at least one test element that is capable ofdetecting a substance using an input of a biological parameter from thepatient that is indicative of smoking behavior. The detector unitanalyzes the biological input from the patient, such as expired gas fromthe lungs, saliva, or wavelengths of light directed through or reflectedby tissue. In some embodiments, the detector unit monitors patient SpCOusing PPG. The detector unit may optionally measure a number of othervariables including, but not limited to, SpO2, heart rate, respiratoryrate, blood pressure, body temperature, sweating, heart ratevariability, electrical rhythm, pulse velocity, galvanic skin response,pupil size, geographic location, environment, ambient temperature,stressors, life events, and other suitable parameters. For breath-basedsensors, patient input may include blowing into a tube as part of thedetector unit. For saliva or other body fluid-based sensors, patientinput may include placement of a fluid sample in a test chamber providedin the detector unit.

For light-based sensors such as PPG, patient input may include placementof an emitter-detector on a finger or other area of exposed skin. Thedetector unit logs the date and time of day, quantifies the presence ofthe targeted substance, and stores the data for future analysis and/orsends the data to another location for analysis, e.g., device 104 orserver 106. The communications unit includes appropriate circuitry forestablishing a communications link with another device, e.g., device104, via a wired or wireless connection. The wireless connection may beestablished using WI-FI, BLUETOOTH, radio frequency, or another suitableprotocol.

FIG. 11 depicts an illustrative embodiment 300 of suitable wavelengthsfor analyzing SpO2 and SpCO using a light-based sensor. SpCO for apatient may be measured by intermittently testing the patient's exhaledbreath with a suitable sensor. In another example, SpCO for the patientmay be measured using a transcutaneous method such asphotoplethysmography (PPG). SpCO is detected by passing light throughpatient tissue, e.g., ear lobe, ear pinna, fingertip, toe, a fold ofskin, or another suitable body part, and analyzing attenuation ofvarious wavelengths. SpO2 is typically measured using two wavelengths,e.g., 302 (660 nm) and 306 (940 nm). SpCO may be measured using threewavelengths, e.g., 302 (660 nm), 304 (810 nm), and 306 (940 nm), or upto seven or more wavelengths, e.g., ranging from 500-1000 nm. Such a PPGsensor may be implemented via finger clips, bands, adhesively appliedsensor pads, or another suitable medium. The PPG sensor may betransmissive, such as used in many pulse oximeters. In transmissive PPGsensors, two or more waveforms of light are transmitted through patienttissue, e.g., a finger, and a sensor/receiver on the other side of thetarget analyzes the received waveforms to determine SpCO. Alternatively,the PPG sensor may be reflective. In reflectance PPG sensors, light isshined against the target, e.g., a finger, and the receiver/sensor picksup reflected light to determine the measurement of SpCO. More detailsare provided below.

Transcutaneous or transmucosal sensors are capable of non-invasivelydetermining blood CO level and other parameters based on analysis of theattenuation of light signals passing through tissue. Transmissivesensors are typically put against a thin body part, such as the earlobe, ear pinna, fingertip, toe, a fold of skin, or another suitablebody part. Light is shined from one side of the tissue and detected onthe other side. The light diodes on one side are tuned to a specific setof wavelengths. The receiver or detector on the other side detects whichwaveforms are transmitted and how much they are attenuated. Thisinformation is used to determine the percentage binding of O2 and/or COto hemoglobin molecules, i.e., SpO2 and/or SpCO.

Reflectance sensors may be used on a thicker body part, such as thewrist. The light that is shined at the surface is not measured at theother side but instead at the same side in the form of light reflectedfrom the surface. The wavelengths and attenuation of the reflected lightis used to determine SpO2 and/or SpCO. In some embodiments, issues dueto motion of the patient wrist are corrected using an accelerometer. Forexample, the information from the accelerometer is used to correcterrors in the SpO2 and SpCO values due to motion. Examples of suchsensors are disclosed in U.S. Pat. No. 8,224,411, entitled “NoninvasiveMulti-Parameter Patient Monitor.” Another example of a suitable sensoris disclosed in U.S. Pat. No. 8,311,601, entitled “Reflectance and/orTransmissive Pulse Oximeter”. These two U.S. patents are incorporated byreference herein in their entireties, including all materialsincorporated by reference therein.

In some embodiments, device 102 or 202 is configured to recognize aunique characteristic of the patient, such as a fingerprint, retinalscan, voice label or other biometric identifier, in order to preventhaving a surrogate respond to the signaling and test prompts to defeatthe system. For this purpose, a patient identification sub-unit may beincluded in device 102 or 202. Persons of ordinary skill in the art mayconfigure the identification sub-unit as needed to include one or moreof a fingerprint scanner, retinal scanner, voice analyzer, or facerecognition as are known in the art. Examples of suitable identificationsub-units are disclosed, for example in U.S. Pat. No. 7,716,383,entitled “Flash-interfaced Fingerprint Sensor,” and U.S. PatentApplication Publication No. 2007/0005988, entitled “MultimodalAuthentication,” each of which is incorporated by reference herein intheir entirety.

The identification sub-unit may include a built in still or video camerafor recording a picture or video of the patient automatically as thebiological input is provided to the test element. Regardless of the typeof identification protocol used, device 102 or 202 may associate theidentification with the specific biological input, for example by timereference, and may store that information along with other informationregarding that specific biological input for later analysis.

A patient may also attempt to defeat the detector by blowing into thedetector with a pump, bladder, billows, or other device, for example,when testing exhaled breath. In the embodiment of saliva testing, apatient may attempt to substitute a clean liquid such as water. Forlight-based sensors, the patient may ask a friend to stand in for him orher. Means to defeat these attempts may be incorporated into the system.For example, device 102 or 202 may incorporate the capability ofdiscerning between real and simulated breath delivery. Thisfunctionality may be incorporated by configuring the detector unit tosense oxygen and carbon dioxide, as well as the target substance (e.g.,carbon monoxide). In this manner, the detector unit can confirm that thegas being analyzed is coming from expired breath having lower oxygen andhigher carbon dioxide than ambient air. In another example, the detectorunit may be configured to detect enzymes naturally occurring in salivaso as to distinguish between saliva and other liquids. In yet anotherexample, light-based sensors may be used to measure blood chemistryparameters other than CO level and thus results may be compared to knownsamples representing the patient's blood chemistry.

In some embodiments, device 104 (e.g., a smart phone) receivesmeasurements from device 102 (e.g., a wearable device) in real time,near real time, or periodically according to a suitable interval. Device104 may provide a user interface for prompting a patient for certaininputs. Device 104 may provide a user interface for displaying certainoutputs of the collected data. Device 104 may permit the patient toinput information that the patient believes relevant to his or hercondition without prompting or in response to prompting. Suchinformation may include information about the patient's state of mindsuch as feeling stressed or anxious. Such unprompted information may becorrelated to a biological input based on a predetermined algorithm,such as being associated with the biological input that is closest intime to the unprompted input or associated with the first biologicalinput occurring after the unprompted input. Server 106 (e.g., ahealthcare database server) may receive such data from one or both ofdevices 102 and 104. In some embodiments, the data may be stored on acombination of one or more of devices 102, 104, and 106. The data may bereported to various stakeholders, such as the patient, patient's doctor,peer groups, family, counselors, employer, and other suitablestakeholders.

In some embodiments, a wearable device, e.g., device 102 or 202, may beapplied to patients during, e.g., their usual annual visits, to detectsmoking behavior and then refer the smokers to quit programs. Thepatient is provided with a wearable device to wear as an outpatient fora period of time, e.g., one day, one week, or another suitable period oftime. Longer wear times may provide more sensitivity in detection ofsmoking behavior and more accuracy in quantifying the variables relatedto smoking behavior. FIG. 15 below provides an illustrative flow diagramfor detecting smoking behavior and will be described in more detailbelow.

In some embodiments, employers ask employees to voluntarily wear thewearable device for a period of time, such as one day, one week, oranother suitable period of time. The incentive program may be similar toprograms for biometric screening for obesity, hyperlipidemia, diabetes,hypertension, and other suitable health conditions. In some embodiments,health care insurance companies ask their subscribers to wear thewearable device for a suitable period of time to detect smokingbehavior. Based on the smoking behavior being quantified, these patientsmay be referred to a smoking cessation program as described in thepresent disclosure.

When wearing the wearable device for a suitable period of time, e.g.,five days, a number of parameters may be measured real-time or near realtime. These parameters may include, but are not limited to, CO, eCO,SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters. FIG. 12 shows illustrative chart 400 for apatient's varying levels of SpCO for a typical five-day monitoringperiod. Data points 402 and 404 indicate high level of CO which in turnlikely indicates high smoking events. Data points 406 and 408 indicatelow level of CO likely because the patient was asleep or otherwiseoccupied. One or more algorithms may be applied to the granular datapoints on the curve to detect a smoking event with adequate sensitivityand specificity. For example, the algorithms may analyze one or more ofshape of the SpCO curve, start point, upstroke, slope, peak, delta,downslope, upslope, time of change, area under curve, and other suitablefactors, to detect the smoking event.

Data from the wearable device may be sent to a smart phone, e.g., device104, or a cloud server, e.g., server 106 or 204, either in real time, atthe end of each day, or according to another suitable time interval. Thesmart phone may measure parameters, including but not limited to,movement, location, time of day, patient entered data, and othersuitable parameters. The patient entered data may include stressors,life events, location, daily events, administrations of nicotine patchesor other formulas, administrations of other drugs for smoking cessation,and other suitable patient entered data. For example, some of thepatient entered data may include information regarding phone calls,athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. The received data may be compiled,analyzed for trends, and correlated either real time or after the periodof time is complete.

From the parameters measured above, information regarding smoking may bederived via a processor located in, e.g., device 102, 104, or 202, orserver 106 or 204. For example, the processor may analyze theinformation to determine CO trends, averages, peaks, and associations,other vital sign trends during day, and how do those vitals changebefore, during and after smoking. FIG. 13 shows an illustrative diagram500 for the analyzed information. The patient may arrive at FIG. 13 byzooming in on a given day in FIG. 12. Data point 502 indicates the SpCOlevel when the patient is asleep. Data point 504 shows that when thepatient wakes up, the SpCO level is the lowest. Data points 506, 508,and 510 indicate high SpCO levels are associated with triggers such aswork breaks, lunch, and commute. The processor may analyze the SpCOtrends in FIG. 13 to determine parameters such as total number ofcigarettes smoked, average number of cigarettes smoked per day, maximumnumber of cigarettes smoked per day, intensity of each cigarette smoked,quantity of each cigarette smoked, what that patient's smoking eventlooks like on the curve to be used later for quit program, time of day,day of week, associated stressors, geography, location, and movement.For example, the total number of peaks in a given day may indicate thenumber of cigarettes smoked, while the gradient of each peak mayindicate the intensity of each cigarette smoked.

FIG. 14 depicts an illustrative data structure for storing patient data.In this embodiment, data structure 600 illustrates patient data 602associated with data points in FIG. 13, e.g., data point 508. Patientdata 602 includes identifying information for the patient such aspatient name 604 and patient age 606. Patient data 602 includes curvedata 608 corresponding to the curve in FIG. 13. For example, curve data608 includes curve identifier 610 corresponding to data point 508. Thedata corresponding to data point 508 may be collected by device 102,104, or 202, and/or server 106 or 204 or a combination thereof. Dataassociated with curve identifier 610 includes day, time, and locationinformation 612. The data includes patient vital signs such as CO and O2levels 614. The data includes patient entered data such as trigger 616.The patient entered data may be entered in response to a prompt to thepatient on, e.g., device 104, or entered without prompting on thepatient's volition. Curve data 608 includes curve identifier 618 foradditional data points in FIG. 13. Data structure 600 may be adapted asappropriate for storing patient data.

FIG. 15 depicts an illustrative flow diagram 700 for detecting smokingbehavior of a patient over a suitable evaluation period. When thepatient wears the wearable device for a suitable period of time, e.g.,five days, a number of parameters may be measured in real-time, nearreal time, at the end of each day, or according to another suitable timeinterval. These parameters may include, but are not limited to, CO, eCO,SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters. The wearable device or another suitable device maymeasure parameters, including but not limited to, movement, location,time of day, and other suitable parameters.

At step 702, a processor in a smart phone, e.g., device 104, or a cloudserver, e.g., server 106 or 204, receives the described patient data. Atstep 704, the processor receives patient entered data in response to aprompt displayed to the patient on, e.g., a smart phone, and/or patientdata entered without a prompt on the patient's volition. The patiententered data may include stressors, life events, location, daily events,administrations of nicotine patches or other formulas, administrationsof other drugs for smoking cessation, and other suitable patient entereddata. At step 706, the processor sends instructions to update a patientdatabase with the received data. For example, the processor may transmitthe patient data to a healthcare provider server or a cloud server thathosts the patient database.

At step 708, the processor analyzes the patient data to determinesmoking events. The processor may compile the data, analyze the data fortrends, and correlate the data either real time or after the evaluationperiod is complete. For example, the processor may analyze theinformation to determine CO trends, averages, peaks, shape of curve, andassociations, other vital sign trends during day, and how those vitalschange before during and after smoking. The processor may analyze theSpCO trends to determine parameters such as total number of cigarettessmoked, average number of cigarettes smoked per day, maximum number ofcigarettes smoked per day, intensity of each cigarette smoked, time ofday, day of week, associated stressors, geography, location, andmovement. For example, the total number of peaks in a given day mayindicate the number of cigarettes smoked, while the gradient of eachpeak may indicate the intensity of each cigarette smoked.

At step 710, the processor transmits the determined smoking events andrelated analysis to the patient database for storage. At step 712, theprocessor determines whether the evaluation period has ended. Forexample, the evaluation period may be five days or another suitable timeperiod. If the evaluation period has not ended, the processor returns tostep 702 to receive additional patient data, analyze the data, andupdate the patient database accordingly.

If the evaluation period has ended, at step 714, the processor ends thedata collection and analysis. For example, the processor may evaluateall collected data at the end of the evaluation period to prepare areport as described with respect to FIG. 16 below.

It is contemplated that the steps or descriptions of FIG. 15 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 15 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 15.

In some embodiments, the systems and methods described herein providefor initiating and setting up a quit program for a patient. After thepatient has completed a five-day evaluation while wearing the wearabledevice, e.g., device 102 or 202, the full dataset is compiled andanalyzed by the system and delivered to the patient or a doctor for thequit program. FIG. 16 shows an illustrative embodiment 800 of a samplereport from the analysis. For example, the report indicates that, fromOctober 1 to October 6, Mr. Jones smoked a total of 175 cigarettes withan average number of 35 cigarettes smoked per day, and a maximum numberof 45 cigarettes smoked in one day. Mr. Jones' CO level averaged at 5.5%with a maximum of 20.7% and stayed above 4% for 60% of duration of thefive-day evaluation period. Mr. Jones' triggers included work, homestressors, and commute. The report recommends a high dose and frequencynicotine level prediction for commencing nicotine replacement therapy inview of Mr. Jones' smoking habits.

In some embodiments, the patient works with their doctor or counselor tobegin the process to enter the quit program. In some embodiments, thesystem sets up a quit program automatically based on the data from theevaluation period. The sample report in FIG. 16 is one example ofmeasuring SpCO and producing a report on CO exposure, associatedstressors, and predicting a starting nicotine dose requirement. Forexample, a high volume and intensity smoker may be more nicotinedependent at quit program entry, which the processor can estimate basedon five-day behavior, and the quit program would start the patient on ahigher nicotine replacement therapy dose. This may avoid many patientsfailing early in a quit program due to withdrawal symptoms. Based on thereport data, including average and maximum number of cigarettes smoked,SpCO levels, triggers, the processor may determine the dosage fornicotine for administration to the patient. For example, the processormay determine a high dosage of nicotine for patients that on averagesmoke more than a threshold number of cigarettes per day. As the reportdata is updated, the processor may update the dosage for nicotine aswell.

The collected data may impact the quit program initiation and set-up forthe patient immediately before they enter the program by assisting indrug selection and dosing. For example, indication of higher smoking mayprompt starting on higher nicotine replacement therapy dose or multipledrugs (e.g., adding medication used to treat nicotine addiction, such asvarenicline). The collected data may impact the quit program initiationand set-up by determining frequency, type, and duration of counselingrequired for the patient. The data may lead to stratification of smokerneeds. For example, highest risk smokers with highest use may get moreinterventions while lower risk smokers may get fewer interventions. Forexample, interventions may include a text message, a phone call, asocial networking message, or another suitable event, from the patient'sspouse, friend, doctor, or another suitable stakeholder, at certaintimes of days when the patient is likely to smoke.

The collected data may impact the quit program initiation and set-up bycorrelating smoking behavior with all variables above such as stressorsprompting smoking, time of day, and other suitable variables used forcounseling the patient up front to be aware of these triggers.Counseling interventions may target these stressors and there may beinterventions aimed at those times of day for the patient, such as atext message or call at those times of days. The collected data mayimpact the quit program initiation and set-up by assigning peer groupsbased on smoking behavior. The collected data may be used to predictand/or avert a smoking event. For example, if tachycardia or heart ratevariability or a suitable set of variables precedes most smoking events,this will sound an alarm and the patient may administer a dose of drugor can receive a call from a peer group, doctor, or counselor. FIG. 20shows an illustrative embodiment of preempting a smoking event and willbe discussed in more detail below. FIGS. 22 and 23 show illustrativeflow diagrams for predicting and preventing an expected smoking eventand will be discussed in more detail below.

In some embodiments, the systems and methods described herein providefor maintaining participation in the quit program for the patient. Oncein the quit program, the patient may continue to wear the wearabledevice, e.g., device 102 or 202, for monitoring. The system may employanalytic tools such as setting an SpCO baseline and tracking progressagainst this baseline. The trend may drop to zero and stay there(indicating no more smoking). The trend may drop slowly with peaks andvalleys (indicating reduction in smoking). The trend may drop to zerothen spike for a recurrence (indicating a relapse).

The system may employ patient engagement strategies by providing smallinfrequent rewards for group or individual progress to engage thepatient. The system may provide employer rewards, payers, spouse, orpeer groups to engage the patient. The system may present the processfor the patient as a game and improve visibility of progress. FIG. 21provides an illustrative embodiment of such a user interface and will bediscussed in more detail below. In some embodiments, the system maytransmit the data in real time to a health care provider for remotemonitoring and allowing the provider to efficiently monitor and adjustpatient care without having to have them in the office every day. Forexample, the provider may send instructions to the system to adjustmedication type and dose, alter intensity of counseling, call and textfor positively encouraging progress, or trigger an intervention if thepatient is failing to refrain from smoking. This may supplant staffedquit phone lines which are expensive and may efficiently automate theprocess. The system may employ increased intensity and frequency toimprove outcomes in patients. The system may encourage the patient viasupport from spouse, employer, health care provider, peers, friends, andother suitable parties via scheduled phone calls, text messages, orother suitable communications.

FIG. 17 shows an illustrative graph 900 for tracking the average dailySpCO trend for a patient running up to and then entering a quit program.The average trend is tracked for each day as it improves. The doctor orcounselor may zoom in on a particular day (present or past) to see thegranular detail and associations of CO with other parameters measuredand associated stressors 910. Visibility of the trend of CO over time inthe quit program may prevent patient dropouts, prevent smoking relapse,titrate drugs and counseling, and improve outcomes. For example, datapoint 902 indicates CO level before the patient entered the quitprogram. Data points 904 and 906 indicate CO levels as nicotinereplacement therapy and varenicline therapy is administered during thequit program. Data point 908 indicates the patient has successfully quitsmoking. At this point, the system may recommend the patient enter intoa recidivism prevention program to prevent relapse.

In some embodiments, the systems and methods described herein providefor a follow up program after a patient successfully quits smoking.After a successful quit, verified by the system, the patient wears thewearable device, e.g., device 102 or 202, for an extended period oftime, e.g., a few months to two years, as an early detection system forrelapse. The system may collect data and employ counseling strategies asdescribed above for the quit program.

In some embodiments, a patient receives a wearable device, e.g., device102 or 202, and an app for their smartphone, e.g., device 104, thatallows them to assess health remotely and privately by tracking severaldifferent parameters. The patient may submit breath samples or put theirfinger in or on a sensor on the wearable device several times per day asrequired. They may wear the wearable device to get more frequent or evencontinuous measurements. At the end of a test period, e.g., five toseven days or another suitable time period, the processor in the smartphone may calculate their CO exposure and related parameters. FIG. 18shows an exemplary app screen 1000 showing measurements such as SpCO1002, SpO2 1004, heart rate 1006, respiratory rate 1008, blood pressure1010, and body temperature 1012. Warning indicators 1014 and 1016 may beprovided for atypical measurements, possibly indicating effects ofsmoking on the body. The system may prompt the patient with alerts whenwarning indicators 1014 or 1016 are activated.

The system may recommend the patient enter a smoking cessation programand provide options for such programs. The patient may agree to enter aquit program on seeing such objective evidence of smoking. The systemmay share this data with the patient's spouse, their doctor, or anothersuitable stakeholder involved in the patient's quit program. Forexample, the system may share the data with an application astakeholder's mobile device or send a message including the data viaemail, phone, social networking, or another suitable medium. Triggers toget a patient to join the quit program may include spousal suggestion,employer incentive, peer pressure, personal choice, an illness, oranother suitable trigger. The patient may initiate the quit program ontheir own or bring the data to a doctor to receive assistance in joininga quit program.

While the patient is initiated in the quit program, the wearable device,e.g., device 102 or 202, may continue to monitor the patient's healthparameters, such as heart rate, movement, location that precede thesmoking behavior, and transmit the data to the patient and/or his doctorto improve therapy. The smart phone app on, e.g., device 104, mayreceive patient entered data including, but not limited to, stressors,life events, location, daily events, administrations of nicotine patchesor other formulas, administrations of other drugs for smoking cessation,and other suitable patient entered data.

FIG. 19 shows an illustrative embodiment of an app screen 1100 forreceiving patient entered data. App screen 1100 may be displayed whenthe smart phone app receives an indication of a smoking event, e.g., dueto a spike in the CO level for the patient. App screen 1100 prompts theuser to enter a trigger for the smoking event. For example, the patientmay select from one of options 1102, 1104, 1106, and 1108 as triggeringa smoking event or select option 1110 and provide further informationregarding the trigger. Other triggers for a smoking event may includephone calls, athletics, sport, stress, sex, and other suitable patiententered data. The patient may voluntarily invoke app screen 1100 as wellto enter trigger information for a smoking event. In some embodiments,app screen 1100 for receiving patient data is displayed to the patientduring the five-day evaluation period to collect information regardingsmoking behavior before the patient enters the quit program.

In some embodiments, the collected data is used by the smart phone appto avert a smoking event. The processor running the app or a processorin another device, such as device 102 or 202 or server 106 or 204, mayanalyze the information regarding what happens to heart rate and othervital signs in the period leading up to a smoking event. The processormay correlate changes in heart rate, such as tachycardia, that canpredict when a patient will smoke. This information may be used toinitiate a prevention protocol for stopping the smoking event. Forexample, the prevention protocol may include delivering a bolus ofnicotine. The nicotine may be delivered via a transdermal patch or atransdermal transfer from a reservoir of nicotine stored in the wearabledevice, e.g., device 102 or 202. In another example, the preventionprotocol may include calling the patient's doctor, a peer group, oranother suitable stakeholder. The processor may send an instruction toan automated call system, e.g., resident at server 106 or 204, toinitiate the call. FIGS. 22 and 23 provide flow diagrams for predictinga smoking event based on patient vital signs and will be described inmore detail below.

FIG. 20 shows an illustrative embodiment of an app screen 1200implementing such a prevention protocol. For example, if a patient tendsto become tachycardic twenty minutes before every cigarette, theprocessor may detect tachycardia and prompt the patient to administernicotine via option 1202. The patient may vary the nicotine dose viaoption 1204. In some embodiments, the nicotine is administeredautomatically. The amount may be determined based on the patient'scurrent SpCO level or another suitable parameter. The patient mayreceive a call from a peer group via option 1206, a doctor via 1208, oranother suitable stakeholder. The caller may provide the patientencouragement to abstain from smoking and suggest seeking out otheractivities to divert the patient's attention.

In some embodiments, the smart phone app presents the process for thepatient as a game to improve visibility of progress. The app may employpatient engagement strategies by providing small frequent or infrequentrewards for group or individual progress to engage the patient. The appmay provide employer rewards, payers, spouse, or peer groups to engagethe patient. FIG. 21 shows an illustrative app screen 1300 for such anembodiment. App screen 1300 offers the patient a reward for abstainingfrom smoking for fifteen days. Prompt 1302 challenges the patient tofurther abstain for another fifteen days. The patient may select option1304 to accept the reward and continue monitoring progress while heremains smoke free. However, the patient may be having difficultyabstaining and may select option 1306 to be contacted a peer group, acounselor, a family member, a doctor, or another suitable party.

In some embodiments, the patient is a peer and supporter for others intheir group. Groups can track each other's progress and give support.For example, the group members may be part of a social network thatallows them to view each other's statistics and provide encouragement toabstain from smoking. In another example, a message, e.g., a tweet, maybe sent to group members of the patient's social network, e.g.,followers, when it is detected the patient is smoking. The message mayinform the group members that the patient needs help. The group mayconnect to the patient in a variety of ways to offer help. Thisinteraction may enable to the patient to further abstain from smokingthat day.

In some embodiments, at a primary care visit a patient provides a sampleand is asked if they smoke. For example, the wearable device, e.g.,device 102 or 202, is applied to the patient and receives the sample fora one time on-the-spot measurement of the patient's SpCO level. The SpCOlevel may exceed a certain threshold which suggests that the patientsmokes. FIG. 24 provides a flow diagram for the one-time measurement ofthe patient's SpCO level. The patient may be provided with the wearabledevice to wear as an outpatient for a period of time, e.g., one day, oneweek, or another suitable period of time. Longer wear times may providemore sensitivity in detection of smoking behavior and more accuracy inquantifying the variables related to smoking behavior.

The wearable device, e.g., device 102 or 202, and smart phone app on,e.g., device 104, may continue to monitor the patient's healthparameters, such as SpCO level, in real time or near real time andprocess the data for observation by the patient, the doctor, or anyother suitable party. The smart phone app may also offer the data in adigestible form for daily or weekly consumption by the patient and/orthe doctor. For example, the smart phone app may generate displaysimilar to FIG. 17 showing daily progress with the option to zoom into aparticular day for observing further details. The doctor may log thepatient into a healthcare database stored at, e.g., server 106 or 204 incommunication with a mobile device running the smart phone app, andcontinue to receive the data from the smart phone app via the Internetor another suitable communications link. The smart phone app may receivedata from the sensors via a wired connection to the mobile devicerunning the app or via a wireless connection such as WI-FI, BLUETOOTH,radio frequency, or another suitable communications link.

The patient and the doctor may set a future quit date and send thepatient home without any drugs or with drugs to help the patient quit.The patient may begin working towards the agreed quit date. Feedbackfrom the wearable device and/or the smart phone app may assist thepatient to be more prepared at the quit date to actually quit as well asto smoke less at the quit date than when they started at the start. Oncethe patient starts the quit program, they may get daily or weeklyfeedback from their spouse, doctor, nurse, counselor, peers, friends, orany other suitable party.

Drug therapy, if prescribed, may be based by the doctor or may beadjusted automatically based on patient performance. For example, thedoctor may remotely increase or decrease nicotine dose administrationbased on the patient's CO, eCO, SpCO level. In another example, aprocessor in the wearable device, e.g., device 102 or 202, the smartphone, e.g., device 104, or a remote server, e.g., server 106 or 204,may increase or decrease nicotine dose administration based on CO trendsfrom the patient's past measurements. Similarly, the drug therapy may beshortened or lengthened in duration according to collected data.

FIG. 22 depicts an illustrative flow diagram 1400 for predicting asmoking event based on a patient's CO, eCO, SpCO measurements and othersuitable factors. The patient may be given a wearable device, e.g.,device 102 or 202, and a smart phone app for their mobile phone, e.g.,device 104. The wearable device may include a PPG sensor for measuringthe patient's SpCO level. At step 1402, a processor in the wearabledevice or the patient's mobile phone receives a PPG measurement for thepatient's SpCO level and associated time and location. The processor mayalso receive other information such as heart rate, respiration rate, andother suitable factors in predicting a smoking event.

At step 1404, the processor updates a patient database that is storedlocally or at a remote location, such as a healthcare database in server106, with the received patient data. At step 1406, the processoranalyzes the current and prior measurements for the patient parametersand determines whether a smoking event is expected. For example, theSpCO trend may be at a local minimum which indicates the user may bereaching for a cigarette to raise their SpCO level. The processor mayapply a gradient descent algorithm to determine the local minimum. Atstep 1408, the processor determines whether the SpCO trend indicates anexpected smoking event. If the processor determines a smoking event isnot expected, at step 1410, the processor determines if the time and/orlocation are indicative of an expected smoking event. For example, theprocessor may determine that the patient typically smokes when they wakeup in the morning around 7 a.m. In another example, the processor maydetermine that the patient typically smokes soon after they arrive atwork. In yet another example, the processor may determine that thepatient typically smokes in the evening whenever they visit a particularrestaurant or bar.

If the processor determines a smoking event is expected from either step1408 or 1410, at step 1412, the processor initiates a preventionprotocol for the patient to prevent the smoking event. Informationregarding the prevention protocol may be stored in a memory of device102, 104, or 202, or server 106 or 204, or a combination thereof. Theinformation for the prevention protocol may include instructions for oneor more intervention options to initiate when the patient is about tosmoke. For example, the processor may initiate an alarm in the patient'smobile phone and display an app screen similar to FIG. 20. The appscreen may offer the patient options to administer nicotine or receive acall from a peer group, a doctor, or another suitable party.Alternatively, the prevention protocol may include automaticallyadministering a bolus of nicotine to the patient from a reservoir ofnicotine stored in the patient's wearable device. In another example,the app screen may indicate that a message, e.g., a tweet, will be sentto group members of the patient's social network, e.g., followers, whenit is detected the patient has failed to abstain from smoking. Thepatient may refrain from smoking to prevent the message indicating hisfailure from being sent out.

In some embodiments, steps 1408 and 1410 are combined into one step orinclude two or more steps for a processor determining that a smokingevent is expected. For example, the processor may determine that asmoking event is expected based on a combination of the SpCO trend, thepatient's location, and/or the current time. In another example, theprocessor may determine that a smoking event is expected based on aseries of steps for analyzing one or more of the patient's SpCO, SpO2,heart rate, respiratory rate, blood pressure, body temperature,sweating, heart rate variability, electrical rhythm, pulse velocity,galvanic skin response, pupil size, geographic location, environment,ambient temperature, stressors, life events, and other suitableparameters.

At step 1414, the processor determines whether the prevention protocolwas successful. If a smoking event occurred, at step 1418, the processorupdates the patient database to indicate that the prevention protocolwas not successful. If a smoking event did not occur, at step 1416, theprocessor updates the patient database to indicate that the preventionprotocol was successful. The processor returns to step 1402 to continuereceiving the PPG measurement for the patient's SpCO level andassociated data. The processor may monitor the patient's vital signscontinuously to ensure that the patient does not relapse into a smokingevent.

It is contemplated that the steps or descriptions of FIG. 22 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 22 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 22.

FIG. 23 depicts an illustrative flow diagram 1500 for determiningwhether the prevention protocol was successful in relation to step 1414in FIG. 22. At step 1502, the processor receives patient data fordetermining whether a smoking event occurred. At step 1504, theprocessor analyzes currently received patient data and previouslyreceived patient data. At step 1506, the processor determines whether asmoking event occurred based on the analysis. For example, if nonicotine was administered but the patient's SpCO levels are currentlyhigher than previous SpCO levels, the processor may determine thepatient relapsed and smoked a cigarette. In such a situation, at step1508, the processor returns a message indicating that the preventionprotocol was not successful. In another example, if the patient's vitalsigns indicate no rise or a drop in SpCO levels, the processor maydetermine that a smoking event did not occur. In such a situation, atstep 1510, the processor returns a message indicating that theprevention protocol was successful.

It is contemplated that the steps or descriptions of FIG. 23 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 23 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 23.

FIG. 24 depicts an illustrative flow diagram 1600 for a one-timemeasurement of the patient's SpCO level using a PPG sensor. For example,the wearable device, e.g., device 102 or 202, is applied to the patientand receives the sample for a one-time measurement of the patient's SpCOlevel. At step 1602, a processor in the wearable device receives a PPGmeasurement for the patient's SpCO level and any other suitable data,such as time, location, SpO2, heart rate, respiratory rate, bloodpressure, body temperature, sweating, heart rate variability, electricalrhythm, pulse velocity, galvanic skin response, pupil size, geographiclocation, environment, ambient temperature, stressors, life events, andother suitable parameters. At step 1604, the processor analyzes thereceived data to determine a recent smoking event. For example, anelevated SpCO level beyond a certain threshold may suggest that thepatient has recently smoked a cigarette.

At step 1606, the processor determines whether the patient SpCO levelindicates a smoking event has occurred. For example, the SpCO levelexceeding a specified threshold may indicate a smoking event. In anotherexample, one or more of shape of the SpCO curve, start point, upstroke,slope, peak, delta, downslope, upslope, time of change, area undercurve, and other suitable factors, may indicate a smoking event. One ormore of these factors may assist in quantification of the smoking event.For example, the total number of peaks in a given day may indicate thenumber of cigarettes smoked, while the gradient shape and size and othercharacteristics of each peak may indicate the intensity and amount ofeach cigarette smoked. If the processor determines the SpCO level isindicative that a smoking event has not occurred, at step 1608, theprocessor returns a denial message indicating the patient did not have arecent smoking event. The patient's doctor may find this informationuseful in evaluating the patient's smoking behavior. If the processordetermines the SpCO level is indicative that a smoking event hasoccurred, at step 1610, the processor returns a confirmation messageindicating the patient did have a recent smoking event. In this case,the collected data may be used to set up a quit program for the patientas described above.

After steps 1608 or 1610, at step 1612, the processor updates thepatient database to record this information. At step 1614, the processorterminates the SpCO level evaluation for the patient. The patient may beprovided with the wearable device to wear as an outpatient for a periodof time, e.g., one day, one week, or another suitable period of time.Longer wear times may provide more sensitivity in detection of smokingbehavior and more accuracy in quantifying the variables related tosmoking behavior.

It is contemplated that the steps or descriptions of FIG. 24 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 24 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 24.

In some embodiments, data from one or more devices associated withpatients, such as devices 102 and 104 or device 202, are received at acentral location, such as server 106 or 204. The patient devices log inreal time or near real time multiple biometric and contextual variables.For example, the biometric variables may include CO, eCO, SpCO, SpO2,heart rate, respiratory rate, blood pressure, body temperature,sweating, heart rate variability, electrical rhythm, pulse velocity,galvanic skin response, pupil size, and other suitable biometricvariables. For example, the contextual variables may include GPSlocation, patient activities (e.g., sports, gym, shopping, or anothersuitable patient activity), patient environment (e.g., at work, at home,in a car, in a bar, or another suitable patient environment), stressors,life events, and other suitable contextual variables. The collected datamay also include in-person observation of the patients' smokingbehavior. A spouse or friend or buddy may be able to enter data thattheir patient smoked, correlating that data with the SpCO readings todetermine accuracy.

Server 106 includes a processor for receiving data for multiple patientsover a period of time and analyzes the data for trends that occur aroundthe time of an actual smoking event. Based on the trends, the processordetermines a diagnostic and/or detection test for a smoking event. Thetest may include one or more algorithms applied to the data asdetermined by the processor. For example, the processor may analyze aspike in CO level of a patient. Detecting the spike may includedetermining that the CO level is above a certain specified level.Detecting the spike may include detecting a relative increase in thepatient's CO level from a previously measured baseline. The processormay detect a spike as a change in the slope of the patient's CO trendover a period of time. For example, the CO trend moving from a negativeslope to a positive slope may indicate a spike in the CO level. Inanother example, the processor may apply one or more algorithms tochanges in heart rate, increasing heart rate variability, changes inblood pressure, or variation in other suitable data in order to detect asmoking event.

FIG. 25 depicts an illustrative flow diagram 1700 for detecting asmoking event as described above. A processor (e.g., in server 106 or204) may determine a diagnostic and/or detection test for a smokingevent according to flow diagram 1700. At step 1702, the processorreceives current patient data. At step 1704, the processor retrievespreviously stored data for the patient from a database, e.g., a patientdatabase stored at server 106 or 204. At step 1706, the processorcompares the current and prior patient data to detect a smoking event.For example, the processor may analyze a spike in CO level of thepatient. Detecting the spike may include detecting a relative increasein the patient's CO level from a previously measured baseline. Theprocessor may detect a spike as a change in the slope of the patient'sCO trend over a period of time. For example, the CO trend moving from anegative slope to a positive slope may indicate smoking behavior. Inanother example, the processor may apply one or more algorithms tochanges in heart rate, increasing heart rate variability, changes inblood pressure, or variation in other suitable data in order to detect asmoking event. At step 1708, the processor determines whether a smokingevent occurred based on, e.g., a spike in CO level of the patient asdescribed. If no smoking event is detected, at step 1710, the processorreturns a message indicating that a smoking event did not occur. If asmoking event is detected, at step 1712, the processor returns a messageindicating that a smoking event occurred. At step 1714, the processorupdates the patient database with the results from either step 1710 or1712.

It is contemplated that the steps or descriptions of FIG. 25 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 25 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 25.

In some embodiments, the processor analyzes initially received data tomeasure when a person smokes and ties the algorithm to a variable thattriggers the algorithm for diagnosing and/or detecting a smoking event.The processor continues to analyze other variables as additional patientdata is received. The processor may determine another variable thatchanges when the patient smokes and instead use that variable to triggerthe algorithm. For example, the processor may opt to use the othervariable because it is less invasive or easier to measure than theinitially selected variable.

In some embodiments, the algorithm for detecting a smoking event has ahigh sensitivity. Sensitivity is defined as a percentage of the numberof actual smoking events detected by the sensor and algorithm. Forexample, if a patient smokes 20 times in one day, and the algorithmidentifies every smoking event, it is 100% sensitive.

In some embodiments, the algorithm for detecting a smoking event has ahigh specificity. Specificity is defined as the ability of the test tonot make false positive calls of a smoking event (i.e., positive testwith no smoking event present). If the sensor and algorithm do not makeany false positive calls in a day, it has 100% specificity.

In another example, if a patient smokes 20 times and the algorithmidentifies 18 of the 20 actual smoking events and indicates 20 otherfalse smoking events, it has 90% sensitivity (i.e., detected 90% ofsmoking events) and 50% specificity (i.e., over called the number ofsmoking events by 2×).

In some embodiments, after the processor determines one or morealgorithms and applies to SpCO measurements to detect a smoking eventwith adequate sensitivity and specificity, the processor determineswhether there is an association of other biometric variables orcontextual variables with the SpCO results that could be used on theirown (without SpCO) to detect a smoking event. The processor maydetermine another variable that changes when the patient smokes andinstead use that variable to trigger the algorithm. For example, theprocessor may opt to use the other variable because it is less invasiveor easier to measure or more reliable than the initially selectedvariable.

In some embodiments, the processor analyzes received patient data topredict a smoking event likelihood before it happens. The processor mayanalyze received patient data over a period of time, e.g., five minutes,10 minutes, 15 minutes, 20 minutes, or another suitable time interval,before a smoking event to determine one or more triggers. For example,some smoking events may be preceded by contextual triggers (e.g., at abar, before, during, or after eating, before, during, or after sex, oranother suitable contextual trigger). In another example, some smokingevents may be preceded by changes in a biometric variable, e.g., heartrate or another suitable biometric variable. The determined variablesmay overlap with those selected for diagnosis and detection andtherefore may be used for prediction as well. Alternatively, thedetermined variables may not overlap with those selected for diagnosis.

The processor may inform patients of a smoking event likelihood andtrigger a prevention protocol (e.g., as discussed with respect to FIGS.22 and 23) to prevent smoking change behavior. The processor detectssmoking events for a patient entered in, e.g., a quit program, andtracks and analyses trends in received patient data. The processor maydetermine goals for the patient and reward them when he achieves the setgoals (e.g., as discussed with respect to FIG. 21). The processor maypredict when a patient is about to smoke and intervene just in time bysuggesting a call to a peer group or a physician or by administering abolus of nicotine (e.g., as discussed with respect to FIG. 20).

In one example, the processor predicts smoking events for the patientbased on 75% of the patient's smoking events, during diagnosis, beingpreceded by increased heart rate (or a suitable change in anothervariable). During the quit program, the processor may apply one or morealgorithms to received patient data to predict smoking events andinitiate a prevention protocol. For example, the prevention protocol mayengage the patient just in time by putting the patient in contact withsupporters, such as a doctor, a counselor, a peer, a team member, anurse, a spouse, a friend, a robot, or another suitable supporter. Insome embodiments, the processor applies algorithms to adjust settings,such as baseline, thresholds, sensitivity, and other suitable settings,for each patient based on their five-day run-in diagnostic period. Theprocessor may then use these customized algorithms for the specificpatient's quit program. The described combination of techniques to altersmoking behavior in a patient may be referred to as a digital drug.

In some embodiments, the processor detects smoking in a binary mannerwith a positive or a negative indication. The processor initially usesobservational studies and SpCO measurements from the patient to detectsmoking behavior. For example, the processor receives data regardingtrue positives for smoking events from observational data for thepatient's smoking behavior. The processor determines if detection basedon SpCO measurements matches true positives for smoking events. If thereis a match, the processor applies algorithms to other received patientdata including patient's SpCO, SpO2, heart rate, respiratory rate, bloodpressure, body temperature, sweating, heart rate variability, electricalrhythm, pulse velocity, galvanic skin response, pupil size, geographiclocation, environment, ambient temperature, stressors, life events, andother suitable parameters. The processor determines whether any patternsin non-SpCO variable data are also indicative of a smoking event. Suchvariables may be used in algorithms for non-SpCO devices, such aswearable smart watches or heart rate monitor straps or other devices, todetect smoking events.

The processor may quantify smoking behavior when it is detected based onthe received patient data. For example, the processor analyzes SpCO datatrends to indicate how intensely the patient smoked each cigarette, howmany cigarettes the patient smoked in one day, how much of eachcigarette was smoked, and/or how long it took to smoke each cigarette.The processor may use other biometric or contextual variables for theindications as well. The processor uses the received patient data topredict the likelihood for a smoking event to occur in the near future,e.g., in the next 10 minutes. The processor may analyze the receivedpatient data over a preceding period of time, e.g., five minutes, 10minutes, 15 minutes, 20 minutes, or another suitable time interval,before a smoking event to determine one or more triggers.

In some embodiments, the systems and methods described herein providefor evaluating smoking behavior of a patient. During a five-day testingperiod, the patient behaves as they normally would. Devices 102, 104,and/or 106 or devices 202 and/or server 204 receive patient datarelating to the patient's smoking behavior. There is very little to noengagement of the patient as the purpose of the testing period is toobserve the patient's smoking patterns. The testing period may beextended to a second five-day period if needed. Alternatively, the firstand second periods may be shorter, e.g., two or three days, or longer,e.g., a week or more. Before the second testing period, the processordetermines a model of how the patient smokes.

In the second testing phase, the processor applies a series ofperturbations to the model to see if the smoking behavior changes. Theremay be several types of perturbations, each with several dimensions. Forexample, the perturbation may be whether sending a text message beforeor during a smoking event causes the smoking event to be averted orshortened. Dimensions within the perturbation may be different senders,different timing, and/or different content for the text messages. Inanother example, the perturbation may be whether a phone call at certaintimes of the day or before or during a smoking event causes the smokingevent to be averted or shortened. Dimensions within the perturbation maybe different callers, different timing, and/or different content for thephone calls. In yet another example, the perturbation may be whetheralerting the patient to review their smoking behavior at several pointsin the day averts smoking for a period of time thereafter. Dimensionsmay include determining whether and when that aversion extinguishes. Inother examples, the perturbations may be rewards, team play, or othersuitable triggers to avert or shorten the patient's smoking events.

In some embodiments, the processor delivers perturbations to the smokingmodel for the patient using a machine learning process. The machinelearning process delivers perturbations, tests the results, and adjuststhe perturbation accordingly. The processor determines what works bestto achieve an identified behavior change by trying options via themachine learning process. The machine learning process may be appliedduring the second testing phase as slight perturbations. The machinelearning process may be also be applied with significant perturbationsduring the patient's quit phase to increase efforts to try to get thepatient to quit smoking or to continue to abstain from smoking.

FIG. 26 depicts an illustrative flow diagram 1800 for applying one ormore perturbations to the smoking model for the patient in the secondtesting phase. At step 1802, a processor in wearable device 102 or 202,mobile device 104, or server 106 or 204 receives patient data relatingto the patient's smoking behavior in the first testing phase. At step1804, the processor analyzes the received patient data to determine amodel for the patient's smoking behavior. At step 1806, the processorapplies one or more perturbations to the model to see if the smokingbehavior changes. The perturbation may be applied to the model using amachine learning process. There may be several types of perturbations,each with several dimensions. For example, the perturbation may bewhether sending a text message before or during a smoking event causesthe smoking event to be averted or shortened. Dimensions within theperturbation may be different senders, different timing, and/ordifferent content for the text messages.

At step 1808, the processor determines whether the perturbation alteredthe patient's smoking behavior. For example, the processor determineswhether receiving a text message before or during a smoking event causedthe patient to abstain from or shorten his smoking. If the perturbationcaused a change in the patient's smoking behavior, at step 1810, theprocessor updates the model for the patient's smoking behavior toreflect the positive result of the applied perturbation. The processorthen proceeds to step 1812. Otherwise, the processor proceeds directlyto step 1812 from step 1808 and determines whether to apply anotherperturbation or a variation in the dimensions of the presentperturbation. The processor may use the machine learning process todetermine whether to apply additional perturbations to the model. If nomore perturbations need to be applied, at step 1814, the processor endsthe process of applying perturbations.

If more perturbations need to be applied, at step 1816, the processordetermines another perturbation to apply to the model. For example, theprocessor may adjust the present perturbation to send a text message tothe patient at a different time or with different content. In anotherexample, the processor may apply a different perturbation by initiatinga phone call to the patient before or during a smoking event. Theprocessor returns to step 1806 to apply the perturbation to the model.The processor may use the machine learning process to deliver aperturbation, test the result, and adjust the perturbation or selectedanother perturbation accordingly. In this manner, the processordetermines what works best to achieve an identified behavior change forthe patient by trying different options via the machine learningprocess.

It is contemplated that the steps or descriptions of FIG. 26 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 26 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.9 (e.g., device 102, 104, or 106) or FIG. 10 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 26.

In an illustrative example, a 52-year old male patient is incentivizedby his employer to get screened for smoking behavior. The patient entersan evaluation program on Jun. 1, 2015. The patient reports smoking 20cigarettes per day. The program coordinator, such as a physician orcounselor, loads an app on the patient's smart phone, e.g., mobiledevice 104, and gives the patient a connected sensor, e.g., wearabledevice 102 or 202. The coordinator informs the patient to smoke andbehave normally for a five-day testing period and respond to promptsfrom the app as they arise. After the five-day period is over, thecoordinator enters the patient into a supplementary testing period wherethe app prompts a bit more often (e.g., to apply perturbations). Thecoordinator informs the patient that it is up to him at that point torespond however he wishes. The coordinator establishes a targeted dateof Jun. 10, 2015 to include 10 days of testing.

After the five-day testing period, the coordinator receives a report(e.g., a five-day report card as discussed with respect to FIG. 16). Thereport indicates 150 cigarette smoking events detected using CO ascompared to 100 cigarette smoking events based on the patient'sestimate. The report indicates that associated contextual variablesinclude alcohol, location, stress, and other suitable data. The reportindicates that associated biometric variables include increased heartrate, without exercise, as preceding 50% of smoking events. The reportindicates that prompts for stress levels showed increased stress in 20%of smoking events.

During the supplementary five-day test period, a processor in the mobiledevice, e.g., device 104, the wearable device, e.g., device 102 or 202,or a remote server, e.g., server 106 or 204, applies perturbations via amachine learning process. For example, the mobile device prompts thepatient four-time times a day with a display including number ofcigarettes smoked, intensity of smoking, and time of day. As the dayprogresses, the prompts cause the patient to reduce smoking for longerperiods of time. The net effect is that the patient smokes fewercigarettes in the second half of day as compared to the first half. Inanother example, the mobile device prompts the patient at 10 am everyday with a display including number of cigarettes smoked the previousday. The net effect is studied as to how the prompt impacts thepatient's smoking behavior for the rest of day. The machine learningprocess may adjust time and content of the display to alter thedimensions of the perturbation as required.

In another example, the processor applies a perturbation via a machinelearning process in the form of a text message sent to the patientduring a smoking event. The machine learning process varies thedimensions of the perturbation by having different senders, differenttiming, sending before or during smoking, different content of message,different images in the message, and/or different rewards forabstaining. In another example, the processor applies a perturbation viaa machine learning process in the form of a phone call to the patientduring a smoking event. The machine learning process varies thedimensions of the perturbation by having different callers, differenttiming, calling before or during smoking, different content of call,different tones in the call, and/or different rewards for abstaining.

In another example, the processor applies a perturbation via a machinelearning process in the form of a prompt for a particular activity onthe patient's mobile device. The prompt indicates that the patient issmoking but should consider smoking only half a cigarette and then getoutside. During long times between cigarette events, or when an event ispredicted, the machine learning process applies perturbations to attemptto avert the smoking event completely. For example, the mobile devicedisplays a prompt notifying the patient that they are in a high-riskzone and should consider an alternative activity or location or phone afriend.

After the testing period, the coordinator enters the patient into thequit program. During the quit period, the processor receives patientdata and applies algorithms to the data as described. The processor usesall data from the first and second testing periods to customize thealgorithms and starting regimen and quit program interventions for thespecific patient. The diagnostic and detection algorithms may use one ormore biometric variables for the patient, such as SpCO, to detectsmoking behavior. The quit program includes a nicotine regimen startingon day one as part of the nicotine replacement therapy. The nicotine maybe delivered via a transdermal patch or a transdermal transfer from areservoir of nicotine stored in the wearable device given to thepatient. The processor applies algorithms to the received patient datato determine the most effective interventions. The processor applies theinterventions and further adjusts them as required. The processor mayset goal event counts and determine which method works best for alteringthe patient's smoking behavior. The processor may invoke multiplepersonalized interventions from stakeholders as perturbations via themachine learning process and test which works best for altering thepatient's smoking behavior. The perturbations with the most impact onthe patient's smoking model may be retained, while those with less or noimpact may not be used further.

While exemplary embodiments of the systems and methods described abovefocus on smoking behaviors, examples of which include but are notlimited to smoking of tobacco via cigarettes, pipes, cigars, and waterpipes, and smoking of illegal products such as marijuana, cocaine,heroin, and alcohol related behaviors, it will be immediately apparentto those skilled in the art that the teachings of the present inventionare equally applicable to any number of other undesired behaviors. Suchother examples include: oral placement of certain substances, withspecific examples including but not limited to placing chewing tobaccoand snuff in the oral cavity, transdermal absorption of certainsubstances, with specific examples including but not limited toapplication on the skin of certain creams, ointments, gels, patches orother products that contain drugs of abuse, such as narcotics, and LSD,and nasal sniffing of drugs or substances of abuse, which includes butis not limited to sniffing cocaine.

In general, the basic configuration of devices 102 and 104 or device202, as well as related steps and methods as disclosed herein will besimilar as between the different behaviors that are being addressed. Thedevices may differ somewhat in design to account for different targetsubstances that are required for testing or different testingmethodology necessitated by the different markers associated withparticular undesired behaviors.

It will also be appreciated by persons of ordinary skill in the art thata patient participating in a formal cessation program may take advantageof the systems and methods disclosed herein as adjuncts to the cessationprogram. It will be equally appreciated that the patient may beindependently self-motivated and thus beneficially utilize the systemsand methods for quitting the undesired behavior unilaterally, outside ofa formal cessation program.

In further exemplary embodiments, the systems and methods disclosedherein may be readily adapted to data collection and in particular tocollection of reliable and verifiable data for studies related toundesired behaviors for which the present invention is well suited totest. Such studies may be accomplished with virtually no modification tothe underlying device or methods except that where treatment was notincluded there would not necessarily be a need for updating of the testprotocol or treatment protocol based on user inputs.

FIG. 27 illustrates another variation of a system and/or method foraffecting an individual's smoking behavior using a number of the aspectsdescribed herein as well as further quantifying an exposure of theindividual to cigarette smoke. In the illustrated example, a pluralityof samples of biometric data are obtained from the individual andanalyzed to quantify the individual's exposure to cigarette smoke suchthat the quantified information can be relayed to the individual, amedical caregiver, and/or other parties having a stake in theindividual's health. The example discussed below employs a portabledevice 1900 that obtains a plurality of samples of exhaled air from theindividual with commonly available sensors that measure an amount ofcarbon monoxide within the sample of exhaled air (also referred to asexhaled carbon monoxide or ECO). However, the quantification andinformation transfer are not limited to exposure of smoking based onexhaled air. As noted above, there are many sampling means to obtain anindividual's smoking exposure. The methods and devices described in thepresent example can be combined or supplemented with such sampling meanswhere possible while still remaining with the scope of the invention. Inaddition, while the present example discusses the use of a portablesampling unit, the methods and procedures described herein can be usedwith a dedicated or non-portable sampling unit.

The measurement of exhaled CO level has been known to serve as animmediate, non-invasive method of assessing a smoking status of anindividual. See for example, The Measurement of Exhaled Carbon Monoxidein Healthy Smokers and Non-smokers, S. Erhan Devecia, et al., Departmentof Public Health, Medical Faculty of Firat University, Elazig, Turkey2003 and Comparison of Tests Used to Distinguish Smokers fromNonsmokers, M. J. Jarvis et al. American Journal of Public Health,November 1987, V77, No. 11. These articles discuss that exhaled CO(“eCO”) levels for non-smokers can range between 3.61 ppm and 5.6 ppm.In one example, the cutoff level for eCO was above 8-10 ppm to identifya smoker.

Turning back to FIG. 27, as shown a portable or personal sampling unit1900 communicates with either a personal electronic device 110 or acomputer 112. Where the personal electronic device 110 includes, but isnot limited to a smart phone, ordinary phone, cellular phone, or otherpersonal transmitting device exclusively designed for receiving datafrom the personal sampling unit 1900). Likewise, the computer 112 isintended to include a personal computer, local server, or a remoteserver. Data transmission 114 from the personal sampling unit 1900 canoccur to both or either the personal electronic device 110 and/or thecomputer 112. Furthermore, synchronization 116 between the personalelectronic device 110 and the computer 112 is optional. Either thepersonal electronic device 110, the computer 112, and/or the personalsampling unit 1900 can transmit data to a remote server for dataanalysis as described herein. Alternatively, data analysis can occur,fully or partially, in a local device (such as the computer or personalelectronic device). In any case, the personal electronic device 110and/or computer 112 can provide information to the individual,caretaker, or other individual as shown in FIG. 27.

In the depicted example of FIG. 27, the personal sampling unit 1900receives a sample of exhaled air 108 from the individual via acollection tube 1902. Hardware within the personal sampling unit 1900includes any commercially available electrochemical gas sensor thatdetects carbon monoxide (CO) gas within the breath sample, commerciallyavailable transmission hardware that transmits data 114 (e.g., viaBluetooth, cellular, or other radio waves to provide transmission ofdata). The transmitted data and associated measurements andquantification are then displayed on either (or both) a computer display112 or a personal electronic device 110. Alternatively, or incombination, any of the information can be selectively displayed on theportable sampling unit 1900.

The personal sampling unit (or personal breathing unit) can also employstandard ports to allow direct-wired communication with the respectivedevices 110 and 112. In certain variations, the personal sampling unit1900 can also include memory storage, either detachable or built-in,such the memory permits recording of data and separate transmission ofdata. Alternatively, the personal sampling unit can allow simultaneousstorage and transmission of data. Additional variations of the device1900 do not require memory storage. In addition, the unit 1900 canemploy any number of GPS components, inertial sensors (to trackmovement), and/or other sensors that provide additional informationregarding the patient's behavior.

The personal sampling unit 1900 can also include any number of inputtrigger (such as a switch or sensors) 1904, 1906. As described below,the input trigger 1904, 1906 allow the individual to prime the device1900 for delivery of a breath sample 108 or to record other informationregarding the cigarette such as quantity of cigarette smoked, theintensity, etc. In addition, variations of the personal sampling unit1900 also associate a timestamp of any inputs to the device 1900. Forexample, the personal sampling unit 1900 can associate the time at whichthe sample is provided and provide the measured or inputted data alongwith the time of the measurement when transmitting data 114.Alternatively, the personal sampling device 1900 can use alternate meansto identify the time that the sample is obtained. For example, given aseries of samples rather than recording a timestamp for each sample, thetime periods between each of the samples in the series can be recorded.Therefore, identification of a timestamp of any one sample allowsdetermination of the time stamp for each of the samples in the series.

In certain variations, the personal sampling unit 1900 is designed suchthat it has a minimal profile and can be easily carried by theindividual with minimal effort. Therefore, the input triggers 1904 cancomprise low profile tactile switches, optical switches, capacitivetouch switches, or any commonly used switch or sensor. The portablesampling unit 1900 can also provide feedback or information to the userusing any number of commonly known techniques. For example, as shown,the portable sampling unit 1900 can include a screen 1908 that showsselect information as discussed below. Alternatively, or in addition,the feedback can be in the form of a vibrational element, an audibleelement, and a visual element (e.g., an illumination source of one ormore colors). Any of the feedback components can be configured toprovide an alarm to the individual, which can serve as a reminder toprovide a sample and/or to provide feedback related to the measurementof smoking behavior. In addition, the feedback components can provide analert to the individual on a repeating basis in an effort to remind theindividual to provide periodic samples of exhaled air to extend theperiod of time for which the system captures biometric (such as eCO, COlevels, etc.) and other behavioral data (such as location either enteredmanually or via a GPS component coupled to the unit, number ofcigarettes, or other triggers). In certain cases, the reminders can betriggered at higher frequency during the initial program or datacapture. Once sufficient data is obtained, the reminder frequency can bereduced.

FIG. 28A illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 27. As discussedabove, an individual provides breath samples using the portable samplingunit. The individual can be reminded at a regular interval or at randomintervals depending upon the nature of the treatment or interventionprogram. Each sample is evaluated by one or more sensors within theportable sampling unit to measure an amount of CO. The CO measurementstypically correspond to the inflection points 410 on the graph of FIG.28A. Each CO measurement 410 corresponds to a timestamp as shown in thehorizontal axis. The data accumulated via the portable sampling unitallows for the collection of a dataset comprising at least the COmeasurement and time of the sample which can be graphed to obtain an eCOcurve which is indicative of the amount of CO attributable to thesmoking behavior of the individual over the course of the time period.

As noted herein, the individual can further track additional informationsuch as smoking of a cigarette. The smoking of the cigarette can beassociated with its own time stamp as shown by bar 414. In one variationof the method and system under the present disclosure, the individualcan use the input triggers on the portable sampling unit to enter thenumber of cigarettes smoked or a fraction thereof. For example, eachactuation of the input trigger can be associated with a fractionalamount of a cigarette (e.g., ½, ⅓, ¼, etc).

FIG. 28B illustrates a portion of a graphic representation of datacollected as described above. However, in this variation, thequantification of an individual's smoking behavior can use behavioraldata to better approximate the CO value between eCO readings. Forexample, in some variations, eCO measurements between any two points 410can be approximated using a linear approximation between the two points.However, it is known that, in the absence of being exposed to new CO,the CO level decay within the bloodstream. This decay can beapproximated using a standard rate, a rate based on the biometricinformation of the patient (weight, heartbeat, activity, etc.) As shownin FIG. 28B, when the patient is between cigarettes 414, the calculatedCO level can follow a decay rate 440. Once the individual records acigarette 414, the CO increase 442 can again be approximated, either byusing a standard rate or one that is calculated using biometric data asdiscussed above, or based on the intensity, duration, and amount ofcigarettes smoked. Accordingly, the methods and system described hereincan optionally use an improved (or approximated) eCO curve 438 using thebehavioral data discussed above. Such an improved eCO rate can also beused to determine an improved eCO curve 438 while the individual sleeps.This improved eCO curve can then provide an improved eCO load asdescribed herein. The biometric information used to determine decay ratecan be measured by the portable sampling device or by external biometricmeasuring devices that communicate with the system.

This approximated or improved eCO curve 438 can be displayed to theindividual (or to a third party) as a means to help change behavior asthe individual can view a real time approximated CO level (i.e., therate of decrease when not smoking and the rate of increase whensmoking). Additional information can also be displayed, for example, thesystem can also calculate the amount of CO increase with each cigarettebased on their starting CO value.

FIG. 29 illustrates an example of a dataset used to determine the eCOcurve 412 over a period of time where the eCO attributable to thesmoking behavior of the individual can be quantified over variousintervals of time to determine an eCO Burden or eCO Load for eachinterval. As shown, the period of time extends along the horizontal axisand comprises historical and ongoing data captured/transmitted by theportable sampling unit. In order to provide more effective feedback tothe individual regarding their smoking behavior, the eCO curve 412during a certain time interval can be quantified. In the illustratedexample, the interval of time between times 416 and 418 comprises a24-hour interval of time. A subsequent 24-hour interval is definedbetween times 418 and 420. The interval of time or time interval cancomprise any time between two points within the period of time spannedby the dataset. In most cases, the interval of time will be compared toother intervals of time having the same time duration (i.e., where eachinterval can comprise M minutes, H hours, D days, etc.).

One way of quantifying the eCO Burden/Load over the interval of time isto obtain the area defined by or underneath the eCO curve 412 between agiven interval of time (e.g., 416 to 418, 418 to 420, etc.) using thedataset as shown in the graph of FIG. 29. In the illustrated example,the eCO Burden/Load 422 for the first interval (416 to 418) comprises 41(measured in COppm*t), while the eCO Burden 422 for the second interval(418 to 420) comprises 37. As noted above, along with the eCOBurden/Load 422, the dataset can include the number of cigarettes smoked414 along with the timestamp of each cigarette. This cigarette data canalso be summarized 426 along with the eCO Burden/Load 422 for any giveninterval of time. In the illustrated example, the eCO Burden/Load is adaily load, which allows the individual to track their CO exposure.Determining a CO Load is a more accurate reflection of total smokeexposure compared to simply counting cigarettes because smokers smokedifferently. One smoker may smoke the entire cigarette fully and deeplyand intensely, while another smokes less deeply and intensely. Whileboth individuals may smoke one pack per day, the former will have a muchhigher Daily CO Load due to the intensity that the smoke is inhaled. COLoad is also important as when an individual becomes a patient in aquit-smoking program. In such a case, the quantification allows acaregiver or counselor to follow the patient along during as the patientreduces their smoking activity. For example, the patient may reduce from20 cigs per day to 18 to 16 and so on. However, at 10 cigs per day, thepatient may still have a Daily CO Load that has not lowered because theyare compensating when the smoke the reduced number of cigarette (i.e.,the patient smokes harder and deeper and more intensely). The patient'sreduced smoking exposure only occurs when their CO load decreases.

The data shown in FIG. 29 is intended for illustration purposes only andthe duration of the period of time for a given dataset depends on theamount of time the individual uses the portable sampling unit to capturethe biometric and behavioral data. Quantifying the exposure of exhaledcarbon monoxide comprises correlating a function of exhaled carbonmonoxide versus time over the period of time using the dataset andobtaining the area under the eCO curve 412. In variations of the methodand system, the eCO curve can be calculated or approximated.

FIG. 30 illustrates an example of displaying the biometric data as wellas various other information for the benefit of the user, caregiver, orother party having an interest in assessing the smoking behavior of theindividual. The data illustrated in FIG. 30 is for purposes ofillustration and can be displayed on the portable electronic device(e.g., see 110 in FIG. 27) or on one or more computers. In addition, anyof the biometric data or other data can be displayed on the portablesampling unit 1900.

FIG. 30 illustrates a “dashboard” view 118 of the individual's smokingbehavioral data including a graphical output 120 of the eCO curve 412over a period of time as well as the cigarette count for any giveninterval of time within the period of time. Graphical output 120 canalso provide a measured or calculated nicotine trend 424. This nicotinetrend 424 can be determined from the number of cigarettes smoked 426rather than being a direct measurement of nicotine.

FIG. 30 also illustrates a second graphical output display 122 of an eCOcurve 412 over an alternate time period. In this example, the firstgraphical display 120 shows the eCO curve 412 over 7 days while thesecond display 122 shows the data over 3 days. The dashboard view 118can also include additional information including the latest eCOBurden/Load 124 (or the latest eCO reading from the latest sample), thenumber of cigarettes 126 over a defined period such as the current day,as well as the amount of nicotine 128. In addition, the dashboard 118can also include a count of the number of samples 130 provided by theindividual over a defined period (such as a daily through monthlycount).

The dashboard 118 can also display information that can assist theindividual in the reduction or cessation of smoking. For example, FIG.30 also shows a cost of cigarettes 132 using the count of the portion ofcigarettes smoked by the individual 126 or 426. The dashboard can alsodisplay social connections 146, 142, 140 to assist in cessation ofsmoking. For example, the dashboard can display a medical practitioneror counselor 140 that can be directly messaged. In addition, informationcan be displayed on social acquaintances 142 that are also trying toreduce their own smoking behavior.

The dashboard 118 can also display information regarding smokingtriggers 134 as discussed above, for the individual as a reminder toavoid the triggers. The dashboard can also provide the user withadditional behavioral information, including but not limited to theresults of behavioral questionnaires 136 that the individual previouslycompleted with his/her medical practitioner or counselor.

The dashboard 118 can also selectively display any of the informationdiscussed herein based on an analysis of the individual. For example, itmay be possible to characterize the individual's smoking behaviors andassociate such behaviors with certain means that are effective inassisting the individual in reducing or ceasing smoking. In these cases,where the individual's behaviors allow for classifying in one or morephenotypes (where the individual's observable traits allow classifyingwithin one or more groups, the dashboard can display information that isfound to be effective for that phenotype. Furthermore, the informationon the dashboard can be selectively adjusted by the user to allow forcustomization that the individual finds to be effective as a non-smokingmotivator.

FIG. 31 shows another variation of a dashboard 118 displaying similarinformation to that shown in FIG. 30. As noted above, the displayedinformation is customizable. For example, this variation illustrates theeCO load 140 in a graphical display that shows historical data(yesterday's load), current eCO Burden or load, as well as a targetlevel for that of non-smokers. As shown in FIGS. 30 and 31, theindividual's previous attempts at quitting smoking 138 can be displayed.In addition, the graphical representation 120 of the eCO trend 412 canbe illustrated with individual eCO readings (of the respective sample)can be displayed with information regarding the smoking times 426 aswell as a graphic showing the time or duration of smoking (as shown bythe circles of varying diameter). As noted above, such information canbe entered by the portable sampling unit and displayed in additionalforms as shown in 126 and 127, which respectively show historical andcurrent data regarding the number of times smoked and the number ofwhole cigarettes smoked.

FIGS. 32A to 32C illustrate another variation of a dataset comprisingexhaled carbon monoxide, collection time, and cigarette data quantifiedand displayed to benefit the individual attempting to understand theirsmoking behavior. FIG. 32A illustrates an example where a patientcollected breath samples over the course of a number of days. Theexample data shown in FIGS. 32A to 32C demonstrate data shown over 21days, but any time range is within the scope of the systems and methodsdescribed herein.

As illustrated in FIG. 32A, the period of time 432 is illustrated alongthe horizontal axis with the time intervals being each day within thetime period. Although not shown, during the early stages of samplecollection, the time period itself can comprise one or more days withthe time interval being a multiple of hours or minutes. Clearly, thelonger the time period the greater the ability of the program to selectmeaningful time intervals within the time period.

FIG. 32A illustrates a variation of a dashboard 118 where smoking data(comprising the total number of cigarettes 428 and an associated curve430) are superimposed on a graph showing an eCO curve 412. As notedabove, the individual provides breath samples on a regular or randombasis. In certain variations, the portable sampling unit (not shown)prompts the individual to provide samples for measurement of CO. Theportable sampling unit allows the samples to be associated with a timestamp and transmits other user generated data as discussed above. The COdata is then quantified to provide a value for the exposure of CO (eCOfor exhaled CO) over an interval of time (e.g., per day as shown in FIG.32A).

FIG. 32A also demonstrates the ability to show historical datasimultaneously with present data. For example, the CO load data 140illustrates the previous day's CO load as well as the highest COreading, lowest CO reading, and average CO reading. Similar historicalis shown regarding the cigarette data as well as the smoking cessationquestionnaire results 136.

FIGS. 32B and 32C illustrate the dataset in graphical form as theindividual decreases his/her smoking behavior. AS shown in FIG. 32C, asthe individual continues to provide samples for measurement of CO, thegraphical representation of the dataset shows the individual'sself-reporting of smoking fewer cigarettes, which is verified throughthe reduced values of the CO load 124.

The systems and methods described herein, namely quantification anddisplay of smoking behavior as well as other behavioral data provide abase for which healthcare professionals can leverage into effectiveprograms designed to reduce the effects of cigarette smoke. For example,the system and methods described herein can be used to simply identify apopulation of smokers from within a general population. Once thispopulation is identified, building the dataset on the individual'sspecific smoking behavior can be performed prior to attempting to enrollthat individual in a smoking cessation program. As noted above, thequantification of the smoking burden (or CO burden) along with the timedata of the smoking activity can be combined with other behavioral datato identify smoking triggers unique to that individual. Accordingly, theindividual's smoking behavior can be well understood by the healthcareprofessional prior to selecting a smoking cessation program. Inaddition, the systems and methods described herein are easily adapted tomonitor an individual's behavior once that individual enters a smokingcessation program and can monitor the individual, once they stopsmoking, to ensure that the smoking cessation program remains effectiveand that the individual refrains from smoking.

In addition, the systems and methods described above regardingquantification of smoking behavior can be used to build, update andimprove the model for smoking behavior discussed above as well as toprovide perturbations to assist in ultimately reducing the individual'ssmoking behavior.

FIGS. 33A-33H illustrate another variation of the systems and methodsdescribed above used to implement a treatment plan for identifying asmoking behavior of an individual for ultimately assisting theindividual with smoking cessation and maintaining the individual'sstatus as a non-smoker.

For example, FIG. 33A illustrates an outline of an exemplary outlinethat incorporates the teachings found herein to provide a multi-phasedregime/program 440 intended to ultimately assist the individual inreducing and/or ceasing the smoking behavior. As shown, each phase 442,444, 446, 448, 450, 452 of the program can be associated with a display438. The illustrated display 438 represents a portable device (e.g., asmartphone, tablet, computer), however, the display can comprise anydisplay, or dedicated electronic device where the user can receiveand/or interact with the user interface and subject matter provided bythe program. The subject matter provided by the program can besmoking-related subject matter that is intended to inform the individualabout the effects of smoking and/or can be subject matter based on thesmoking behavior. In addition, the subject matter can change based onthe tracked behavior of the individual or the subject matter can changebased on other factors that are unrelated to the tracked behavior of theindividual.

The smoking related subject matter can also include information and/orwarnings regarding proper use of devices and systems used to compile thesmoking behavior. For example, the warnings can include warnings againstusing the device/system in those cases, including but not limited to useas a measurement of potential carbon monoxide poisoning, measurement ofnon-cigarette smoke inhalation (e.g., from a fire or chemical release).In some cases, the system can instruct the individual to call emergencymedical services (e.g., 911) in the event non-cigarette CO exposureoccurs. The system can also provide system specific warnings such as awarning against sharing of a breath sensor among different individuals.

Furthermore, the subject matter relayed to the individual can include ageneral reminder that no amount of smoking behavior is safe. Such awarning is intended to prevent an individual attempting to use thesystem to reduce or maintain his/her smoking behavior at a relativelevel that the individual might falsely perceive as being a safe levelof smoking. For instance, such a warning can be triggered at aparticular level of exhaled CO, e.g., 0-6 ppm. The warning would statethat the low levels of CO in the exhaled breath do not indicate that itis safe to initiate or increase smoking, or that the current level issafe. The warning could further state that smoking is harmful to one'shealth and any amount of smoking is unsafe.

In the illustrated example, the phases 442, 444, 446, 448, 450, 452 ofthe program 440 can be broken into separate time spans or periods, whereeach phase offers a different goal that allows the individual to buildand make progress in attempting to moderate their smoking behavior. Forinstance, in an example of the method/system, an initial phase 442allows the user to explore their smoking behavior with little or noattempt at trying to enforce an immediate change in smoking behavior.Such an exploration phase 442 offers information to the individual thatallows the individual to identify their smoking behavior. In theillustrated example shown in FIG. 33A, the program phases are separatedas follows with exemplary time periods: Explore 442 (9 days), Build 444(1 day to 4 weeks), Mobilize 446 (1 week), Quit 448 (1 week), Secure 450(11 weeks), and Sustain 552 (40 weeks). Clearly, any variation of timeperiod can be associated with each phase.

Explore 442 can be used to raise behavior about smoking behavior andspark an interest in the individual to quit. Build 444 can be used tobuild skills to encourage the individual to decide to quit. Mobilize 446can be used to prepare the individual to quit. Quit 448 can be used toassist the individual in quitting. For example, this phase can be usedto provide support to the individual during smoking cessation where thesupport includes facilitating interactive communication with thecounselor, facilitating a peer support interactive communication,displaying informational subject matter to support smoking cessation, ora combination thereof. Secure 450 can be used to provide the individualwith skills to continue to quit smoking. Sustain 452 can be used toprovide support to the individual to prevent relapse and solidify theirnon-smoking behavior.

For example, as discussed above, such a first phase 442 can includerecording a plurality of behavioral data from the individual, where suchbehavioral data can include number of cigarettes smoked, the time(s) acigarette is smoked, the location of the individual, the location of theindividual while smoking, moods, as well as any other data indicative ofa behavior of the individual. In association with the behavioral data,the method can allow the individual to submit a plurality of biologicdata from the individual. For instance, the biologic data can includeexhaled air samples submitted to the electronic devices discussed above.Alternatively, or in combination, the submission of biologic data canoccur passively through any number of sensors that actively measurebiologic information of the individual (e.g., via blood, exhaled air,temperature, etc.).

The biologic information is then quantified, which allows the individualto understand the impact of the smoking exposure. As noted above, inthose cases where the biologic data comprises exhaled carbon monoxide,the quantification of smoking exposure can comprise the exhaled carbonmonoxide load.

Next, the method comprises compiling a behavior summary that combines atleast some of the behavioral data and the smoking exposure. FIG. 33Billustrates a display of an example of a behavior summary thatillustrates smoking exposure/exhaled CO load 124 as well as a number ofbehavioral data, including but not limited to number of cigarettessmoked, estimated cost of smoking, and time since last cigarette smoked.The visual display can also provide various menu options 462 to allow anindividual to interact between various subject matter items related tosmoking behavior as well as a counselor. The display 438 can also allowthe individual to view a behavior summary based on daily values, or overa set period of time (e.g., 7 days, 30 days, full history, etc.)

It is noted that the system can also assess the submitted data (eitherbiologic and/or behavioral) to ensure accuracy of the data. For example,the system can assess the time span between submitted samples andprovide a warning if the samples are submitted at undesirable intervals.For instance, the system can provide a warning to the individual ifobtaining the plurality of biologic data from the individual occurswithin a pre-determined time of a previous submission of biologic data.In some cases, especially for biologic data, submission of sampleswithout allowing a sufficient time between samples can lower theeffectiveness of the measurement. In additional variations, the warningto the individual can further comprise rejecting at least one of theplurality of biologic data from the individual in addition to providingthe warning. Such a warning can be provided visually, audibly, sensory,and/or through the visual display of subject matter discussed herein.

FIG. 33C illustrates an additional example of a behavior summarycomprising behavioral data 460 in conjunction with biologic data 124. Inthis example, the user is able to choose between a display of biologicdata (e.g., exhaled carbon monoxide load) and the number of cigarettessmoked. In addition, the display 438 allows for the user to interactwith the data by selecting specific information such as a smoking mapthat shows behavioral data in the form of smoking locations 464. Thepresent disclosure includes any number of variations of displayingeither all or at least a portion of the behavior summary to theindividual to inform the individual about the smoking behavior.

FIG. 33D illustrates another example of the method disclosed hereinusing one or more interactive activities to engage the individual duringthe program. In this variation, the interactive activities can be spreadthrough a first phase of the program. For example, while the first phaseof the program can comprise any time span, in the illustrated variation,the first phase is separated into 9 days with each day having a marker468, 470, 474 that represents that day's activities. The markers can beinteractive, meaning that they allow the individual to access anactivity, or the markers can be on-directional in providing the userwith information. Regardless, the activities can provide subject matterto the individual regarding smoking behavior. For instance, as shown inFIG. 33D, the initial activity 468 can serve as a reminder or triggerthe individual to begin the submission of biologic and/or behavioraldata as described above and the method can produce subject matter thatincorporates any of the data to provide feedback to the user. In FIG.33D, the first activity 468 provides subject matter feedback to the userregarding the need for biologic samples (e.g., breath samples) and canprovide any information related to the biologic or behavioral samples,such as a current count, a minimum number of required samples, or acountdown until the minimum number of samples are met.

FIG. 33D also illustrates additional markers 468, 474 that representadditional activities and/or days of the first phase of the program. Asshown in FIG. 33D, the subject matter 472 can be purely informationalsuch as providing information on how measured CO is a useful indicatorof the individual's exposure to toxins in cigarettes. In othervariations, as shown in FIG. 33E, the subject matter can compriseinteractive activities. For example, as shown in FIG. 25E the middlescreen image prompts an individual for the cost associated withcigarettes or smoking and can calculate the information as shown. Theactivity can then combine the prompted information, as shown in theright screen, to provide additional information indicative of theindividual's smoking behavior 478. For instance, in this example, theinformation comprises the estimated cost of smoking, the reasons forsmoking, and an estimated extrapolated savings upon quitting smoking.The interactive activity can also provide a reward 480 to the individualfor providing biologic and/or behavioral data.

FIG. 33F illustrates additional markers 482, 486, 488, 490, and 496representing subjects such as reasons for smoking 482, addition 486,household 488, time (spent smoking) 490, and confidence 496. As shown,the displayed subject matter can be purely informational (e.g.,displaying reasons for smoking 484) or can be combined with data enteredduring the program (e.g., displaying the amount of time spent smokingper week 492).

FIG. 33G illustrates another activity (associated with activity six 488of FIG. 33F), in this example, as shown in the middle screen, theinteractive data prompted by the program relates to environmentalfactors associated with the individual (e.g., household information).Once the individual enters the environmental information, the programcan then combine the prompted environmental information, as shown in theright screen, to provide additional information indicative of theindividual's smoking behavior 478 using the environmental data.

FIG. 33H represents activities or days seven through nine 490, 496, 498.As the first program phase approaches an end, the individual can beprompted for their confidence in their ability to quit smoking 494 whichcan be affected given that the program will have provided the individualwith the above metrics regarding his/her smoking behavior. Once theinitial program phase ends, as denoted by a completion marker 498, theindividual will have a personalized smoking behavior profile compiledusing metrics specific to that individual. The interactive activitiescan then prompt the individual to enter the next phase of the program(as outlined in FIG. 33A).

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Combination of the aspect of the variations discussed above as wellcombinations of the variations themselves are intended to be within thescope of this disclosure.

Various changes may be made to the invention described and equivalents(whether recited herein or not included for the sake of some brevity)may be substituted without departing from the true spirit and scope ofthe invention. Also, any optional feature of the inventive variationsmay be set forth and claimed independently, or in combination with anyone or more of the features described herein. Accordingly, the inventioncontemplates combinations of various aspects of the embodiments orcombinations of the embodiments themselves, where possible. Reference toa singular item, includes the possibility that there are plural of thesame items present. More specifically, as used herein and in theappended claims, the singular forms “a,” “an,” “said,” and “the” includeplural references unless the context clearly dictates otherwise.

What is claimed is:
 1. A method of enhancing an electronic interactionbetween a coach-counselor assisting an individual-user participating ina behavioral-modification program, the method comprising: providingelectronic access to a database of information during the electronicinteraction between the coach-counselor and the individual-user, wherethe database of information includes a plurality of user-specific inputdata specific to the individual-user, where the plurality ofuser-specific input data includes a subset of individual-user biologicalinput data and at least one of a subset of individual-user psychographicinformation and a subset of individual-user personal information, whereat least a portion of the plurality of user-specific input data ispreviously collected; electronically displaying a background data to thecoach-counselor during the electronic interaction, where the backgrounddata includes a historical information regarding an activity of theindividual-user in the behavioral-modification program, to permit thecoach-counselor a review the historical information regarding theindividual-user during the electronic interaction; electronicallysupplying the coach-counselor with at least one prompt of acommunication topic from a database of generic information applicable tothe behavioral-modification program, where the at least one promptimproves efficiency and accuracy of an interaction between thecoach-counselor and the individual-user to provides a coaching topic forthe coach-counselor to assist the individual-user in thebehavioral-modification program; and electronically transmitting the atleast one prompt to the individual-user as a coach-message.